Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of the Art and Challenges Ahead
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ICIP 2018 Innovation Program: Bringing Industry to ICIP: https://2018.ieeeicip.org/InnovationProgram.asp
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Bandwidth Prediction in Low-Latency Chunked Streaming
HTTP adaptive streaming (HAS) with chunked transfer encoding can be used to reduce latency without sacrificing the coding ef- ficiency. While this allows a media segment to be generated and delivered at the same time, it also causes grossly inaccurate band- width measurements, leading to incorrect bitrate selections. To overcome this effect, we design a novel Adaptive bitrate scheme for Chunked Transfer Encoding (ACTE) that leverages the unique nature of chunk downloads. It uses a sliding window to accurately measure the available bandwidth and an online linear adaptive filter to predict the available bandwidth into the future. Results show that ACTE achieves 96% measurement accuracy, which translates to a 64% reduction in stalls and a 27% increase in video quality.
Docker-Based Evaluation Framework for Video Streaming QoE in Broadband Networks
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Video streaming is one of the top traffic contributors in the Internet and a frequent research subject. It is expected that streaming traffic will grow 4-fold for video globally and 9-fold for mobile video between 2017 and 2022. In this paper, we present an automatized measurement framework for evaluating video streaming QoE in operational broadband networks, using headless streaming with a Docker-based client, and a server-side implementation allowing for the use of multiple video players and adaptation algorithms. Our framework allows for integration with the MONROE testbed and Bitmovin Analytics, which bring on the possibility to conduct large-scale measurements in different networks, including mobility scenarios, and monitor different parameters in the application, transport, network, and physical layers in real-time.Machine Learning Based Video Coding Enhancements for HTTP Adaptive Streaming
Video traffic comprises the majority of today’s Internet traffic, and HTTP Adaptive Streaming (HAS) is the preferred method to deliver video content over the Internet. The increasing demand for video and the improvements in the video display conditions over the years caused an increase in video coding complexity. This increased complexity brought the need for more efficient video streaming and coding solutions. The latest standard video codecs can reduce the size of the videos by using more efficient tools with higher time complexities. The plans for integrating machine learning into upcoming video codecs raised the interest in applied machine learning for video coding. In this doctoral study, we aim to propose applied machine learning methods to video coding, focusing on HTTP adaptive streaming. We present four primary research questions to target different challenges in video coding for HTTP adaptive streaming.
H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...
H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...Alpen-Adria-Universität
HTTP-based Adaptive Streaming (HAS) plays a key role in over-the-top video streaming. It contributes towards reducing the rebuffering duration of video playout by adapting the video quality to the current network conditions. However, it incurs variations of video quality in a streaming session because of the throughput fluctuation, which impacts the user's Quality of Experience (QoE). Besides, many adaptive bitrate (ABR) algorithms choose the lowest-quality segments at the beginning of the streaming session to ramp up the playout buffer as soon as possible. Although this strategy decreases the startup time, the users can be annoyed as they have to watch a low-quality video initially. In this paper, we propose an efficient retransmission technique, namely H2BR, to replace low-quality segments being stored in the playout buffer with higher-quality versions by using features of HTTP/2 including (i) stream priority, (ii) server push, and (iii) stream termination. The experimental results show that H2BR helps users avoid watching low video quality during video playback and improves the user's QoE. H2BR can decrease by up to more than 70% the time when the users suffer the lowest-quality video as well as benefits the QoE by up to 13%.Delivering Traditional and Omnidirectional Media
Universal media access as proposed in the late 90s is now closer to reality. Users can generate, distribute and consume almost any media content, anywhere, anytime and with/on any device. A major technical breakthrough was the adaptive streaming over HTTP resulting in the standardization of MPEG-DASH, which is now successfully deployed in most platforms. The next challenge in adaptive media streaming is virtual reality applications and, specifically, omnidirectional (360°) media streaming.
This tutorial first presents a detailed overview of adaptive streaming of both traditional and omnidirectional media, and focuses on the basic principles and paradigms for adaptive streaming. New ways to deliver such media are explored and industry practices are presented. The tutorial then continues with an introduction to the fundamentals of communications over 5G and looks into mobile multimedia applications that are newly enabled or dramatically enhanced by 5G.
A dedicated section in the tutorial covers the much-debated issues related to quality of experience. Additionally, the tutorial provides insights into the standards, open research problems and various efforts that are underway in the streaming industry.
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ratio than comparable x86 instances for specific workloads. We evaluate in this paper the video encoding performance of x86 and Arm instances of four instance families using the latest FFmpeg version and two video codecs. We examine the impact of the encoding parameters, such as different presets and bitrates, on the time and cost for encoding. Our experiments reveal that Arm instances show high time and cost saving potential of up to
33.63% for specific bitrates and presets, especially for the x264 codec. However, the x86 instances are more general and achieve low encoding times, regardless of the codec.On Optimizing Resource Utilization in AVC-based Real-time Video Streaming
Real-time video streaming traffic and related applications have witnessed significant growth in recent years. However, this has been accompanied by some challenging issues, predominantly resource utilization. IP multicasting, as a solution to this problem, suffers from many problems. Using scalable video coding could not gain wide adoption in the industry, due to reduced compression efficiency and additional computational complexity. The emerging software-defined networking (SDN)and network function virtualization (NFV) paradigms enable re-searchers to cope with IP multicasting issues in novel ways. In this paper, by leveraging the SDN and NFV concepts, we introduce a cost-aware approach to provide advanced video coding (AVC)-based real-time video streaming services in the network. In this study, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder (VTF)functions. At the edge of the network, VRPs are responsible for collecting clients’ requests and sending them to an SDN controller. Then, executing a mixed-integer linear program (MILP) determines an optimal multicast tree from an appropriate set of video source servers to the optimal group of transcoders. The desired video is sent over the multicast tree. The VTFs transcode the received video segments and stream to the requested VRPs over unicast paths. To mitigate the time complexity of the proposed MILPmodel, we propose a heuristic algorithm that determines a near-optimal solution in a reasonable amount of time. Using theMiniNet emulator, we evaluate the proposed approach and show it achieves better performance in terms of cost and resource utilization in comparison with traditional multicast and unicast approaches.
ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Seq...
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HTTP Adaptive Streaming of video content is becoming an integral part of the Internet and accounts for the majority of today’s traffic. Although Internet bandwidth is constantly increasing, video compression technology plays an important role and the major challenge is to select and set up multiple video codecs, each with hundreds of transcoding parameters. Additionally, the transcoding speed depends directly on the selected transcoding parameters and the infrastructure used. Predicting transcoding time for multiple transcoding parameters with different codecs and processing units is a challenging task, as it depends on many factors. This paper provides a novel and considerably fast method for transcoding time prediction using video content classification and neural network prediction. Our artificial neural network (ANN) model predicts the transcoding times of video segments for state-of-the-art video codecs based on transcoding parameters and content complexity. We evaluated our method for two video codecs/implementations (AVC/x264 and HEVC/x265) as part of large-scale HTTP Adaptive Streaming services. The ANN model of our method is able to predict the transcoding time by minimizing the mean absolute error (MAE) to 1.37 and 2.67 for x264 and x265 codecs, respectively. For x264, this is an improvement of 22% compared to the state of the art.Recommended
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HTTP adaptive streaming (HAS) with chunked transfer encoding can be used to reduce latency without sacrificing the coding ef- ficiency. While this allows a media segment to be generated and delivered at the same time, it also causes grossly inaccurate band- width measurements, leading to incorrect bitrate selections. To overcome this effect, we design a novel Adaptive bitrate scheme for Chunked Transfer Encoding (ACTE) that leverages the unique nature of chunk downloads. It uses a sliding window to accurately measure the available bandwidth and an online linear adaptive filter to predict the available bandwidth into the future. Results show that ACTE achieves 96% measurement accuracy, which translates to a 64% reduction in stalls and a 27% increase in video quality.
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Video traffic comprises the majority of today’s Internet traffic, and HTTP Adaptive Streaming (HAS) is the preferred method to deliver video content over the Internet. The increasing demand for video and the improvements in the video display conditions over the years caused an increase in video coding complexity. This increased complexity brought the need for more efficient video streaming and coding solutions. The latest standard video codecs can reduce the size of the videos by using more efficient tools with higher time complexities. The plans for integrating machine learning into upcoming video codecs raised the interest in applied machine learning for video coding. In this doctoral study, we aim to propose applied machine learning methods to video coding, focusing on HTTP adaptive streaming. We present four primary research questions to target different challenges in video coding for HTTP adaptive streaming.
H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...
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HTTP-based Adaptive Streaming (HAS) plays a key role in over-the-top video streaming. It contributes towards reducing the rebuffering duration of video playout by adapting the video quality to the current network conditions. However, it incurs variations of video quality in a streaming session because of the throughput fluctuation, which impacts the user's Quality of Experience (QoE). Besides, many adaptive bitrate (ABR) algorithms choose the lowest-quality segments at the beginning of the streaming session to ramp up the playout buffer as soon as possible. Although this strategy decreases the startup time, the users can be annoyed as they have to watch a low-quality video initially. In this paper, we propose an efficient retransmission technique, namely H2BR, to replace low-quality segments being stored in the playout buffer with higher-quality versions by using features of HTTP/2 including (i) stream priority, (ii) server push, and (iii) stream termination. The experimental results show that H2BR helps users avoid watching low video quality during video playback and improves the user's QoE. H2BR can decrease by up to more than 70% the time when the users suffer the lowest-quality video as well as benefits the QoE by up to 13%.Delivering Traditional and Omnidirectional Media
Universal media access as proposed in the late 90s is now closer to reality. Users can generate, distribute and consume almost any media content, anywhere, anytime and with/on any device. A major technical breakthrough was the adaptive streaming over HTTP resulting in the standardization of MPEG-DASH, which is now successfully deployed in most platforms. The next challenge in adaptive media streaming is virtual reality applications and, specifically, omnidirectional (360°) media streaming.
This tutorial first presents a detailed overview of adaptive streaming of both traditional and omnidirectional media, and focuses on the basic principles and paradigms for adaptive streaming. New ways to deliver such media are explored and industry practices are presented. The tutorial then continues with an introduction to the fundamentals of communications over 5G and looks into mobile multimedia applications that are newly enabled or dramatically enhanced by 5G.
A dedicated section in the tutorial covers the much-debated issues related to quality of experience. Additionally, the tutorial provides insights into the standards, open research problems and various efforts that are underway in the streaming industry.
Where to Encode: A Performance Analysis of Intel x86 and Arm-based Amazon EC2...
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Video streaming became an undivided part of the Internet. To efficiently utilise the limited network bandwidth it is essential to encode the video content. However, encoding is a computationally intensive task, involving high-performance resources provided by private infrastructures or public clouds. Public clouds, such as Amazon EC2, provide a large portfolio of services and instances optimized for specific purposes and budgets. The majority of Amazon’s instances use x86 processors, such as Intel Xeon or AMD EPYC. However, following the recent trends in computer architecture, Amazon introduced Arm based instances that promise up to 40% better cost performance
ratio than comparable x86 instances for specific workloads. We evaluate in this paper the video encoding performance of x86 and Arm instances of four instance families using the latest FFmpeg version and two video codecs. We examine the impact of the encoding parameters, such as different presets and bitrates, on the time and cost for encoding. Our experiments reveal that Arm instances show high time and cost saving potential of up to
33.63% for specific bitrates and presets, especially for the x264 codec. However, the x86 instances are more general and achieve low encoding times, regardless of the codec.On Optimizing Resource Utilization in AVC-based Real-time Video Streaming
Real-time video streaming traffic and related applications have witnessed significant growth in recent years. However, this has been accompanied by some challenging issues, predominantly resource utilization. IP multicasting, as a solution to this problem, suffers from many problems. Using scalable video coding could not gain wide adoption in the industry, due to reduced compression efficiency and additional computational complexity. The emerging software-defined networking (SDN)and network function virtualization (NFV) paradigms enable re-searchers to cope with IP multicasting issues in novel ways. In this paper, by leveraging the SDN and NFV concepts, we introduce a cost-aware approach to provide advanced video coding (AVC)-based real-time video streaming services in the network. In this study, we use two types of virtualized network functions (VNFs): virtual reverse proxy (VRP) and virtual transcoder (VTF)functions. At the edge of the network, VRPs are responsible for collecting clients’ requests and sending them to an SDN controller. Then, executing a mixed-integer linear program (MILP) determines an optimal multicast tree from an appropriate set of video source servers to the optimal group of transcoders. The desired video is sent over the multicast tree. The VTFs transcode the received video segments and stream to the requested VRPs over unicast paths. To mitigate the time complexity of the proposed MILPmodel, we propose a heuristic algorithm that determines a near-optimal solution in a reasonable amount of time. Using theMiniNet emulator, we evaluate the proposed approach and show it achieves better performance in terms of cost and resource utilization in comparison with traditional multicast and unicast approaches.
ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Seq...
ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Seq...Alpen-Adria-Universität
HTTP Adaptive Streaming of video content is becoming an integral part of the Internet and accounts for the majority of today’s traffic. Although Internet bandwidth is constantly increasing, video compression technology plays an important role and the major challenge is to select and set up multiple video codecs, each with hundreds of transcoding parameters. Additionally, the transcoding speed depends directly on the selected transcoding parameters and the infrastructure used. Predicting transcoding time for multiple transcoding parameters with different codecs and processing units is a challenging task, as it depends on many factors. This paper provides a novel and considerably fast method for transcoding time prediction using video content classification and neural network prediction. Our artificial neural network (ANN) model predicts the transcoding times of video segments for state-of-the-art video codecs based on transcoding parameters and content complexity. We evaluated our method for two video codecs/implementations (AVC/x264 and HEVC/x265) as part of large-scale HTTP Adaptive Streaming services. The ANN model of our method is able to predict the transcoding time by minimizing the mean absolute error (MAE) to 1.37 and 2.67 for x264 and x265 codecs, respectively. For x264, this is an improvement of 22% compared to the state of the art.HTTP Adaptive Streaming State of the Art and Challenges Ahead
Real-time entertainment services deployed over the open, unmanaged Internet – streaming audio and video – account now for more than 70% of the Internet traffic and it is assumed that this number will reach 80% by 2021. The technology used for such services is commonly referred to as HTTP Adaptive Streaming (HAS) and is widely adopted by various platforms such as YouTube, Netflix, Flimmit, etc. thanks to the standardization of MPEG-DASH and HLS. This talk will provide an overview of HAS, the state of the art of selected deployment options, and reviews work-in-progress as well challenges ahead. The main challenge can be characterized by the fact that (i) content complexity increases, (ii) delay or latency are vital application requirements, and (iii) Quality of Experience cannot be neglected anymore.
CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...
CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...Alpen-Adria-Universität
Attempting to cope with fluctuations of network conditions in terms of available bandwidth, latency and packet loss, and to deliver the highest quality of video (and audio) content to users, research on adaptive video streaming has attracted intense efforts from the research community and huge investments from technology giants. How successful these efforts and investments are, is a question that needs precise measurements of the results of those technological advancements. HTTP-based Adaptive Streaming (HAS) algorithms, which seek to improve video streaming over the Internet, introduce video bitrate adaptivity in a way that is scalable and efficient.
However, how each HAS implementation takes into account the wide spectrum of variables and configuration options, brings a high complexity to the task of measuring the results and visualizing the statistics of the performance and quality of experience.
In this paper, we introduce CAdViSE, our Cloud-based Adaptive
Video Streaming Evaluation framework for the automated testing
of adaptive media players. The paper aims to demonstrate a test
environment which can be instantiated in a cloud infrastructure,
examines multiple media players with different network attributes
at defined points of the experiment time, and finally concludes the
evaluation with visualized statistics and insights into the results.CAdViSE or how to find the Sweet Spots of ABR Systems
With the recent surge in Internet multimedia traffic, the enhancement and improvement of media players, specifically DASH media players happened at an incredible rate. DASH Media players take advantage of adapting a media stream to the network fluctuations by continuously monitoring the network and making decisions in near real-time. The performance of algorithms that are in charge of making such decisions was often difficult to be evaluated and objectively assessed.
CAdViSE provides a Cloud-based Adaptive Video Streaming Evaluation framework for the automated testing of adaptive media players. In this talk, I will introduce the CAdViSE framework, its application, and propose the benefits and advantages that it can bring to every web-based media player development pipeline. To demonstrate the power of CAdViSE in evaluating Adaptive Bitrate (ABR) algorithms I will exhibit its capabilities when combined with objective Quality of Experience (QoE) models. For this talk, my team at Bitmovin/ATHENA has selected the ITU-T P.1203 (mode 1) model in order to execute experiments and calculate the Mean Opinion Score (MOS), and better understand the behavior of a set of well-known ABR algorithms in a real-life setting. The talk will display how we tested and deployed our framework using a modular architecture into a cloud infrastructure. This method yields a massive growth to the number of concurrent experiments and the number of media players that can be evaluated and compared at the same time, thus enabling maximum potential scalability. In my team’s most recent experiments, we used Amazon Web Services (AWS) for demonstration purposes. Another awesome feature of CAdViSE that will be discussed here is the ability to shape the test network with endless network profiles. To do so, we used a fluctuation network profile and a real LTE network trace based on the recorded internet usage of a bicycle commuter in Belgium.
CAdViSE produces comprehensive logs for each media streaming experimental session. These logs can then be applied against different goals, such as objective evaluation to stitch back media segments and conduct subjective evaluations afterwards. In addition, startup delays, stall events, and other media streaming defects can be imitated exactly as they happened during the experimental streaming sessions.
Objective and Subjective QoE Evaluation for Adaptive Point Cloud Streaming
Volumetric media has the potential to provide the six degrees of freedom (6DoF) required by truly immersive media. However, achieving 6DoF requires ultra-high bandwidth transmissions, which real-world wide area networks cannot provide economically. Therefore, recent efforts have started to target the efficient delivery of volumetric media, using a combination of compression and adaptive streaming techniques. It remains, however, unclear how the effects of such techniques on the user-perceived quality can be accurately evaluated. In this paper, we present the results of an extensive objective and subjective quality of experience (QoE) evaluation of volumetric 6DoF streaming. We use PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple dynamic point cloud objects. By means of a thorough analysis we investigate the perceived quality impact of the available bandwidth, rate adaptation algorithm, viewport prediction strategy, and user's motion within the scene. We determine which of these aspects has more impact on the user's QoE, and to what extent subjective and objective assessments are aligned.
Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC...
Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC...Alpen-Adria-Universität
HTTP/2 has been explored widely for video streaming, but still suffers from Head-of-Line blocking and three-way hand-shake delay due to TCP. Meanwhile, QUIC running on top of UDP can tackle these issues. In addition, although many adaptive bitrate (ABR) algorithms have been proposed for scalable and non-scalable video streaming, the literature lacks an algorithm designed for both types of video streaming approaches. In this paper, we investigate the impact of quick and HTTP/2 on the performance of adaptive bitrate (ABR) algorithms in terms of different metrics. Moreover, we propose an efficient approach for utilizing scalable video coding formats for adaptive video streaming that combines a traditional video streaming approach (based on non-scalable video coding formats) and a retransmission technique. The experimental results show that QUIC benefits significantly from our proposed method in the context of packet loss and retransmission. Compared to HTTP/2, it improves the average video quality and also provides a smoother adaptation behavior. Finally, we demonstrate that our proposed method originally designed for non-scalable video codecs also works efficiently for scalable videos such as Scalable High EfficiencyVideo Coding (SHVC).CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming
CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video StreamingAlpen-Adria-Universität
With the increasing demand for video streaming applications, HTTP Adaptive Streaming (HAS) technology has become the dominant video delivery technique over the Internet. Current HAS solutions only consider either client- or server-side optimization, which causes many problems in achieving high-quality video, leading to sub-optimal users’ experience and network resource utilization. Recent studies have revealed that network-assisted HAS techniques, by providing a comprehensive view of the network, can lead to more significant gains in HAS system performance. In this paper, we leverage the capability of Software-Define Networking (SDN), Network Function Virtualization (NFV), and edge computing to introduce a CDN-Aware QoE Optimization in SDN-Assisted Adaptive Video Streaming framework called CSDN. We employ virtualized edge entities to collect various information items (e.g., user-, client, CDN- and network-level information) in a time-slotted method. These components then run an optimization model with a new server/segment selection approach in a time-slotted fashion to serve the clients’ requests by selecting optimal cache servers (in terms of fetch and transcoding times). In case of a cache miss, a client’s request is served (i) by an optimal replacement quality (only better quality levels with minimum deviation) from a cache server, (ii) by a quality transcoded from an optimal replacement quality at the edge, or (iii) by the originally requested quality level from the origin server. By means of comprehensive experiments conducted on a real-world large-scale testbed, we demonstrate that CSDN outperforms the state-of-the-art in terms of playback bitrate, the number of quality switches, the number of stalls, and bandwidth usage by at least 7.5%, 19%, 19%, and 63%, respectively.20 Years of Streaming in 20 Minutes
20 Years of Streaming in 20 Minutes as presented during mhv2020 (https://mile-high.video/)
WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile D...
Recently, mobile devices have become paramount in online video streaming. Adaptive bitrate (ABR) algorithms of players responsible for selecting the quality of the videos face critical challenges in providing a high Quality of Experience (QoE) for end users. One open issue is how to ensure the optimal experience for heterogeneous devices in the context of extreme variation of mobile broadband networks. Additionally, end users may have different priorities on video quality and data usage (i.e., the amount of data downloaded to the devices through the mobile networks). A generic mechanism for players that enables specification of various policies to meet end users’ needs is still missing. In this paper, we propose a weighted sum model, namely WISH, that yields high QoE of the video and allows end users to express their preferences among different parameters (i.e., data usage, stall events, and video quality) of video streaming. WISH has been implemented into ExoPlayer, a popular player used in many mobile applications. The experimental results show that WISH improves the QoE by up to 17.6% while saving 36.4% of data usage compared to state-of-the-art ABR algorithms and provides dynamic adaptation to end users’ requirements.
EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming
Mobile networks equipped with edge computing nodes enable access to information that can be leveraged to assist client-based adaptive bitrate (ABR) algorithms in making better adaptation decisions to improve both Quality of Experience (QoE) and fairness. For this purpose, we propose a novel on-the-fly edge mechanism, named EADAS (Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming), located at the edge node that assists and improves the ABR decisions on-the-fly. EADAS proposes (i) an edge ABR algorithm to improve QoE and fairness for clients and (ii) a segment prefetching scheme. The results show a QoE increase of 4.6%, 23.5%, and 24.4% and a fairness increase of 11%, 3.4%, and 5.8% when using a buffer-based, a throughput-based, and a hybrid ABR algorithm, respectively, at the client compared with client-based algorithms without EADAS. Moreover, QoE and fairness among clients can be prioritized using parameters of the EADAS algorithm according to service providers’ requirements.
HTTP Adaptive Streaming – Where Is It Heading?
In this contribution, we present selected novel approaches and results of our research work in the \ATHENA Christian Doppler Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services), a major research project at our department jointly funded by public sources and industry. By putting this work also into the context of related ongoing research activities, we aim at working out where HTTP Adaptive Streaming is currently heading.
Towards View-Aware Adaptive Streaming of Holographic Content
Holography is able to reconstruct a three dimensional structure of an object by recording full wave fields of light emitted from the object. This requires a huge amount of data to be encoded, stored, transmitted, and decoded for holographic content, making it a practical usage challenging, specifically for bandwidth-constrained networks and memory-limited devices. In the delivery of holographic content via the internet, bandwidth wastage should be avoided to tackle high bandwidth demands of holography streaming. For real-time applications, encoding time-complexity is also a major problem. In this paper, the concept of Dynamic Adaptive Streaming over HTTP (DASH) is extended to holography image streaming and view-aware adaptation techniques are studied. As each area of a hologram contains information of a specific view and instead of encoding and decoding the entire hologram, just the part required to render the selected view is encoded and transmitted via the network based on the users’ interactivity. Four different strategies, namely, (i) monolithic, (ii) single view, (iii) adaptive view, and (iv) non-real time streaming are explained and compared in terms of (a) bandwidth requirements, (b) encoding time-complexity, and (c) bitrate overhead. Experimental results show that the view-aware methods reduce the required bandwidth for holography streaming at the cost of a bitrate increase.
Understanding Quality of Experience of Heuristic-based HTTP Adaptive Bitrate ...
Understanding Quality of Experience of Heuristic-based HTTP Adaptive Bitrate ...Alpen-Adria-Universität
Adaptive BitRate (ABR) algorithms play a crucial role in delivering the highest possible viewer’s Quality of Experience (QoE) in HTTP Adaptive Streaming (HAS). Online video streaming service providers use HAS – the dominant video streaming technique on the Internet – to deliver the best QoE for their users. Viewer’s delightfulness relies heavily on how the ABR of a media player can adapt the stream’s quality to the current network conditions. QoE for end-to-end video streaming sessions has been evaluated in many research projects to give better insight into the quality metrics. Objective evaluation models such as ITU Telecommunication Standardization Sector (ITU-T) P.1203 allow for the calculation of Mean Opinion Score (MOS) by considering various QoE metrics, and subjective evaluation is the best assessment approach in investigating the end-user opinion over a video streaming session’s experienced quality. We have conducted subjective evaluations with crowdsourced participants and evaluated the MOS of the sessions using the ITU-T P.1203 quality model. This paper’s main contribution is subjective evaluation analogy with objective evaluation for well-known heuristic-based ABRs.What’s new in MPEG?
What's new in MPEG? A brief update about the results of its 131st MPEG meeting featuring:
- Welcome and Introduction: Jörn Ostermann, Acting Convenor of WG11 (MPEG)
- Versatile Video Coding (VVC): Jens-Rainer Ohm and Gary Sullivan, JVET Chairs
- MPEG 3D Audio: Schuyler Quackenbusch, MPEG Audio Chair
- Video-based Point Cloud Compression (V-PCC): Marius, Preda, MPEG 3DG Chair
- MPEG Immersive Video (MIV): Bart Kroon, MPEG Video BoG Chair
- Carriage of Versatile Video Coding (VVC) and Enhanced Video Coding (EVC): Young-Kwon Lim, MPEG Systems Chair
- MPEG Roadmap: Jörn Ostermann, Acting Convenor of WG11 (MPEG)
MPEG Web site: https://mpeg-standards.com/meetings/mpeg-131/
EPIQ'21: Days of Future Past: An Optimization-based Adaptive Bitrate Algorith...
HTTP Adaptive Streaming (HAS) has become a predominant technique for delivering videos in the Internet. Due to its adaptive behavior according to changing network conditions, it may result in video quality variations that negatively impact the Quality of Experience (QoE) of the user. In this paper, we propose Days of Future Past, an optimization- based Adaptive Bitrate (ABR) algorithm over HTTP/3. Days of Future Past takes advantage of an optimization model and HTTP/3 features, including (i) stream multiplexing and (ii) request cancellation. We design a Mixed Integer Linear Programming (MILP) model that determines the optimal video qualities of both the next segment to be requested and the segments currently located in the buffer. If better qualities for buffered segments are found, the client will send corresponding HTTP GET requests to retrieve them. Multiple segments (i.e., retransmitted segments) might be downloaded simultaneously to upgrade some buffered but not yet played segments to avoid quality decreases using the stream multiplexing feature of QUIC. HTTP/3’s request cancellation will be used in case retransmitted segments will arrive at the client after their playout time. The experimental results shows that our proposed method is able to improve the QoE by up to 33.9%.
Dynamic Adaptive Point Cloud Streaming
High-quality point clouds have recently gained interest as an emerg- ing form of representing immersive 3D graphics. Unfortunately, these 3D media are bulky and severely bandwidth intensive, which makes it difficult for streaming to resource-limited and mobile de- vices. This has called researchers to propose efficient and adaptive approaches for streaming of high-quality point clouds.
In this paper, we run a pilot study towards dynamic adaptive point cloud streaming, and extend the concept of dynamic adaptive streaming over HTTP (DASH) towards DASH-PC, a dynamic adaptive bandwidth-efficient and view-aware point cloud streaming system. DASH-PC can tackle the huge bandwidth demands of dense point cloud streaming while at the same time can semantically link to human visual acuity to maintain high visual quality when needed. In order to describe the various quality representations, we pro- pose multiple thinning approaches to spatially sub-sample point clouds in the 3D space, and design a DASH Media Presentation Description manifest specific for point cloud streaming. Our initial evaluations show that we can achieve significant bandwidth and performance improvement on dense point cloud streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied.
Quality Optimization of Live Streaming Services over HTTP with Reinforcement ...
Quality Optimization of Live Streaming Services over HTTP with Reinforcement ...Alpen-Adria-Universität
Recent years have seen tremendous growth in HTTP adaptive live video traffic over the Internet. In the presence of highly dynamic network conditions and diverse request patterns, existing yet simple hand-crafted heuristic approaches for serving client requests at the network edge might incur a large overhead and significant increase in time complexity. Therefore, these approaches might fail in delivering acceptable Quality of Experience (QoE) to end users. To bridge this gap, we propose ROPL, a learning-based client request management solution at the edge that leverages the power of the recent breakthroughs in deep reinforcement learning, to serve requests of concurrent users joining various HTTP-based live video channels. ROPL is able to react quickly to any changes in the environment, performing accurate decisions to serve clients requests, which results in achieving satisfactory user QoE. We validate the efficiency of ROPL through trace-driven simulations and a real-world setup. Experimental results from real-world scenarios confirm that ROPL outperforms existing heuristic-based approaches in terms of QoE, with a factor up to 3.7×.Policy-driven Dynamic HTTP Adaptive Streaming Player Environment
Video streaming services account for the majority of today’s traffic on the Internet. Although the data transmission rate has been increasing significantly, the growing number and variety of media and higher quality expectations of users have led networked media applications to fully or even over-utilize the available throughput. HTTP Adaptive Streaming (HAS) has become a predominant technique for multimedia delivery over the Internet today. However, there are critical challenges for multimedia systems, especially the tradeoff between the increasing content (complexity) and various requirements regarding time (latency) and quality (QoE). This thesis will cover the main aspects within the end user’s environment, including video consumption and interactivity, collectively referred to as player environment, which is probably the most crucial component in today’s multimedia applications and services. We will investigate the methods that can enable the specification of various policies reflecting the user’s needs in given use cases. Besides, we will also work on schemes that allow efficient support for server-assisted, and network-assisted HAS systems. Finally, those approaches will be considered to combine into policies that fit the requirements of all use cases (e.g., live streaming, video on demand, etc.).
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video StreamingAlpen-Adria-Universität
Recently, HTTP Adaptive Streaming (HAS) has become the dominant video delivery technology over the Internet. In HAS, clients have full control over the media streaming and adaptation processes. Lack of coordination among the clients and lack of awareness of the network conditions may lead to sub-optimal user experience, and resource utilization in a pure client-based HAS adaptation scheme. Software-Defined Networking (SDN) has recently been considered to enhance the video streaming process. In this paper, we leverage the capability of SDN and Network Function Virtualization (NFV) to introduce an edge- and SDN-assisted video streaming framework called ES-HAS. We employ virtualized edge components to collect HAS clients’ requests and retrieve networking information in a time-slotted manner. These components then perform an optimization model in a time-slotted manner to efficiently serve clients’ requests by selecting an optimal cache server (with the shortest fetch time). In case of a cache miss, a client’s request is served (i) by an optimal replacement quality (only better quality levels with minimum deviation) from a cache server, or (ii) by the originally requested quality level from the origin server. This approach is validated through experiments on a large-scale testbed, and the performance of our framework is compared to pure client-based strategies and the SABR system [11]. Although SABR and ES-HAS show (almost) identical performance in the number of quality switches, ES-HAS outperforms SABR in terms of playback bitrate and the number of stalls by at least 70% and 40%, respectively. Video complexity analyzer (VCA) for streaming applications
Vignesh V Menon and Hadi Amirpour gave a talk on ‘Video Complexity Analyzer for Streaming Applications’ at the Video Quality Experts Group (VQEG) meeting on December 14, 2021. Our research activities on video complexity analysis were presented in the talk.
HTTP Adaptive Streaming – Quo Vadis? (2023)
Video traffic on the Internet is constantly growing; networked multimedia applications consume a predominant share of the available Internet bandwidth. A major technical breakthrough and enabler in multimedia systems research and of industrial networked multimedia services certainly was the HTTP Adaptive Streaming (HAS) technique. This resulted in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) which, together with HTTP Live Streaming (HLS), is widely used for multimedia delivery in today’s networks. Existing challenges in multimedia systems research deal with the trade-off between (i) the ever-increasing content complexity, (ii) various requirements with respect to time (most importantly, latency), and (iii) quality of experience (QoE). Optimizing towards one aspect usually negatively impacts at least one of the other two aspects if not both. This situation sets the stage for our research work in the ATHENA Christian Doppler (CD) Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services; https://athena.itec.aau.at/), jointly funded by public sources and industry. In this talk, we will present selected novel approaches and research results of the first year of the ATHENA CD Lab’s operation. We will highlight HAS-related research on (i) multimedia content provisioning (machine learning for video encoding); (ii) multimedia content delivery (support of edge processing and virtualized network functions for video networking); (iii) multimedia content consumption and end-to-end aspects (player-triggered segment retransmissions to improve video playout quality); and (iv) novel QoE investigations (adaptive point cloud streaming). We will also put the work into the context of international multimedia systems research.
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HTTP Adaptive Streaming State of the Art and Challenges Ahead
Real-time entertainment services deployed over the open, unmanaged Internet – streaming audio and video – account now for more than 70% of the Internet traffic and it is assumed that this number will reach 80% by 2021. The technology used for such services is commonly referred to as HTTP Adaptive Streaming (HAS) and is widely adopted by various platforms such as YouTube, Netflix, Flimmit, etc. thanks to the standardization of MPEG-DASH and HLS. This talk will provide an overview of HAS, the state of the art of selected deployment options, and reviews work-in-progress as well challenges ahead. The main challenge can be characterized by the fact that (i) content complexity increases, (ii) delay or latency are vital application requirements, and (iii) Quality of Experience cannot be neglected anymore.
CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...
CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...Alpen-Adria-Universität
Attempting to cope with fluctuations of network conditions in terms of available bandwidth, latency and packet loss, and to deliver the highest quality of video (and audio) content to users, research on adaptive video streaming has attracted intense efforts from the research community and huge investments from technology giants. How successful these efforts and investments are, is a question that needs precise measurements of the results of those technological advancements. HTTP-based Adaptive Streaming (HAS) algorithms, which seek to improve video streaming over the Internet, introduce video bitrate adaptivity in a way that is scalable and efficient.
However, how each HAS implementation takes into account the wide spectrum of variables and configuration options, brings a high complexity to the task of measuring the results and visualizing the statistics of the performance and quality of experience.
In this paper, we introduce CAdViSE, our Cloud-based Adaptive
Video Streaming Evaluation framework for the automated testing
of adaptive media players. The paper aims to demonstrate a test
environment which can be instantiated in a cloud infrastructure,
examines multiple media players with different network attributes
at defined points of the experiment time, and finally concludes the
evaluation with visualized statistics and insights into the results.CAdViSE or how to find the Sweet Spots of ABR Systems
With the recent surge in Internet multimedia traffic, the enhancement and improvement of media players, specifically DASH media players happened at an incredible rate. DASH Media players take advantage of adapting a media stream to the network fluctuations by continuously monitoring the network and making decisions in near real-time. The performance of algorithms that are in charge of making such decisions was often difficult to be evaluated and objectively assessed.
CAdViSE provides a Cloud-based Adaptive Video Streaming Evaluation framework for the automated testing of adaptive media players. In this talk, I will introduce the CAdViSE framework, its application, and propose the benefits and advantages that it can bring to every web-based media player development pipeline. To demonstrate the power of CAdViSE in evaluating Adaptive Bitrate (ABR) algorithms I will exhibit its capabilities when combined with objective Quality of Experience (QoE) models. For this talk, my team at Bitmovin/ATHENA has selected the ITU-T P.1203 (mode 1) model in order to execute experiments and calculate the Mean Opinion Score (MOS), and better understand the behavior of a set of well-known ABR algorithms in a real-life setting. The talk will display how we tested and deployed our framework using a modular architecture into a cloud infrastructure. This method yields a massive growth to the number of concurrent experiments and the number of media players that can be evaluated and compared at the same time, thus enabling maximum potential scalability. In my team’s most recent experiments, we used Amazon Web Services (AWS) for demonstration purposes. Another awesome feature of CAdViSE that will be discussed here is the ability to shape the test network with endless network profiles. To do so, we used a fluctuation network profile and a real LTE network trace based on the recorded internet usage of a bicycle commuter in Belgium.
CAdViSE produces comprehensive logs for each media streaming experimental session. These logs can then be applied against different goals, such as objective evaluation to stitch back media segments and conduct subjective evaluations afterwards. In addition, startup delays, stall events, and other media streaming defects can be imitated exactly as they happened during the experimental streaming sessions.
Objective and Subjective QoE Evaluation for Adaptive Point Cloud Streaming
Volumetric media has the potential to provide the six degrees of freedom (6DoF) required by truly immersive media. However, achieving 6DoF requires ultra-high bandwidth transmissions, which real-world wide area networks cannot provide economically. Therefore, recent efforts have started to target the efficient delivery of volumetric media, using a combination of compression and adaptive streaming techniques. It remains, however, unclear how the effects of such techniques on the user-perceived quality can be accurately evaluated. In this paper, we present the results of an extensive objective and subjective quality of experience (QoE) evaluation of volumetric 6DoF streaming. We use PCC-DASH, a standards-compliant means for HTTP adaptive streaming of scenes comprising multiple dynamic point cloud objects. By means of a thorough analysis we investigate the perceived quality impact of the available bandwidth, rate adaptation algorithm, viewport prediction strategy, and user's motion within the scene. We determine which of these aspects has more impact on the user's QoE, and to what extent subjective and objective assessments are aligned.
Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC...
Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC...Alpen-Adria-Universität
HTTP/2 has been explored widely for video streaming, but still suffers from Head-of-Line blocking and three-way hand-shake delay due to TCP. Meanwhile, QUIC running on top of UDP can tackle these issues. In addition, although many adaptive bitrate (ABR) algorithms have been proposed for scalable and non-scalable video streaming, the literature lacks an algorithm designed for both types of video streaming approaches. In this paper, we investigate the impact of quick and HTTP/2 on the performance of adaptive bitrate (ABR) algorithms in terms of different metrics. Moreover, we propose an efficient approach for utilizing scalable video coding formats for adaptive video streaming that combines a traditional video streaming approach (based on non-scalable video coding formats) and a retransmission technique. The experimental results show that QUIC benefits significantly from our proposed method in the context of packet loss and retransmission. Compared to HTTP/2, it improves the average video quality and also provides a smoother adaptation behavior. Finally, we demonstrate that our proposed method originally designed for non-scalable video codecs also works efficiently for scalable videos such as Scalable High EfficiencyVideo Coding (SHVC).CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming
CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video StreamingAlpen-Adria-Universität
With the increasing demand for video streaming applications, HTTP Adaptive Streaming (HAS) technology has become the dominant video delivery technique over the Internet. Current HAS solutions only consider either client- or server-side optimization, which causes many problems in achieving high-quality video, leading to sub-optimal users’ experience and network resource utilization. Recent studies have revealed that network-assisted HAS techniques, by providing a comprehensive view of the network, can lead to more significant gains in HAS system performance. In this paper, we leverage the capability of Software-Define Networking (SDN), Network Function Virtualization (NFV), and edge computing to introduce a CDN-Aware QoE Optimization in SDN-Assisted Adaptive Video Streaming framework called CSDN. We employ virtualized edge entities to collect various information items (e.g., user-, client, CDN- and network-level information) in a time-slotted method. These components then run an optimization model with a new server/segment selection approach in a time-slotted fashion to serve the clients’ requests by selecting optimal cache servers (in terms of fetch and transcoding times). In case of a cache miss, a client’s request is served (i) by an optimal replacement quality (only better quality levels with minimum deviation) from a cache server, (ii) by a quality transcoded from an optimal replacement quality at the edge, or (iii) by the originally requested quality level from the origin server. By means of comprehensive experiments conducted on a real-world large-scale testbed, we demonstrate that CSDN outperforms the state-of-the-art in terms of playback bitrate, the number of quality switches, the number of stalls, and bandwidth usage by at least 7.5%, 19%, 19%, and 63%, respectively.20 Years of Streaming in 20 Minutes
20 Years of Streaming in 20 Minutes as presented during mhv2020 (https://mile-high.video/)
WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile D...
Recently, mobile devices have become paramount in online video streaming. Adaptive bitrate (ABR) algorithms of players responsible for selecting the quality of the videos face critical challenges in providing a high Quality of Experience (QoE) for end users. One open issue is how to ensure the optimal experience for heterogeneous devices in the context of extreme variation of mobile broadband networks. Additionally, end users may have different priorities on video quality and data usage (i.e., the amount of data downloaded to the devices through the mobile networks). A generic mechanism for players that enables specification of various policies to meet end users’ needs is still missing. In this paper, we propose a weighted sum model, namely WISH, that yields high QoE of the video and allows end users to express their preferences among different parameters (i.e., data usage, stall events, and video quality) of video streaming. WISH has been implemented into ExoPlayer, a popular player used in many mobile applications. The experimental results show that WISH improves the QoE by up to 17.6% while saving 36.4% of data usage compared to state-of-the-art ABR algorithms and provides dynamic adaptation to end users’ requirements.
EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming
Mobile networks equipped with edge computing nodes enable access to information that can be leveraged to assist client-based adaptive bitrate (ABR) algorithms in making better adaptation decisions to improve both Quality of Experience (QoE) and fairness. For this purpose, we propose a novel on-the-fly edge mechanism, named EADAS (Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming), located at the edge node that assists and improves the ABR decisions on-the-fly. EADAS proposes (i) an edge ABR algorithm to improve QoE and fairness for clients and (ii) a segment prefetching scheme. The results show a QoE increase of 4.6%, 23.5%, and 24.4% and a fairness increase of 11%, 3.4%, and 5.8% when using a buffer-based, a throughput-based, and a hybrid ABR algorithm, respectively, at the client compared with client-based algorithms without EADAS. Moreover, QoE and fairness among clients can be prioritized using parameters of the EADAS algorithm according to service providers’ requirements.
HTTP Adaptive Streaming – Where Is It Heading?
In this contribution, we present selected novel approaches and results of our research work in the \ATHENA Christian Doppler Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services), a major research project at our department jointly funded by public sources and industry. By putting this work also into the context of related ongoing research activities, we aim at working out where HTTP Adaptive Streaming is currently heading.
Towards View-Aware Adaptive Streaming of Holographic Content
Holography is able to reconstruct a three dimensional structure of an object by recording full wave fields of light emitted from the object. This requires a huge amount of data to be encoded, stored, transmitted, and decoded for holographic content, making it a practical usage challenging, specifically for bandwidth-constrained networks and memory-limited devices. In the delivery of holographic content via the internet, bandwidth wastage should be avoided to tackle high bandwidth demands of holography streaming. For real-time applications, encoding time-complexity is also a major problem. In this paper, the concept of Dynamic Adaptive Streaming over HTTP (DASH) is extended to holography image streaming and view-aware adaptation techniques are studied. As each area of a hologram contains information of a specific view and instead of encoding and decoding the entire hologram, just the part required to render the selected view is encoded and transmitted via the network based on the users’ interactivity. Four different strategies, namely, (i) monolithic, (ii) single view, (iii) adaptive view, and (iv) non-real time streaming are explained and compared in terms of (a) bandwidth requirements, (b) encoding time-complexity, and (c) bitrate overhead. Experimental results show that the view-aware methods reduce the required bandwidth for holography streaming at the cost of a bitrate increase.
Understanding Quality of Experience of Heuristic-based HTTP Adaptive Bitrate ...
Understanding Quality of Experience of Heuristic-based HTTP Adaptive Bitrate ...Alpen-Adria-Universität
Adaptive BitRate (ABR) algorithms play a crucial role in delivering the highest possible viewer’s Quality of Experience (QoE) in HTTP Adaptive Streaming (HAS). Online video streaming service providers use HAS – the dominant video streaming technique on the Internet – to deliver the best QoE for their users. Viewer’s delightfulness relies heavily on how the ABR of a media player can adapt the stream’s quality to the current network conditions. QoE for end-to-end video streaming sessions has been evaluated in many research projects to give better insight into the quality metrics. Objective evaluation models such as ITU Telecommunication Standardization Sector (ITU-T) P.1203 allow for the calculation of Mean Opinion Score (MOS) by considering various QoE metrics, and subjective evaluation is the best assessment approach in investigating the end-user opinion over a video streaming session’s experienced quality. We have conducted subjective evaluations with crowdsourced participants and evaluated the MOS of the sessions using the ITU-T P.1203 quality model. This paper’s main contribution is subjective evaluation analogy with objective evaluation for well-known heuristic-based ABRs.What’s new in MPEG?
What's new in MPEG? A brief update about the results of its 131st MPEG meeting featuring:
- Welcome and Introduction: Jörn Ostermann, Acting Convenor of WG11 (MPEG)
- Versatile Video Coding (VVC): Jens-Rainer Ohm and Gary Sullivan, JVET Chairs
- MPEG 3D Audio: Schuyler Quackenbusch, MPEG Audio Chair
- Video-based Point Cloud Compression (V-PCC): Marius, Preda, MPEG 3DG Chair
- MPEG Immersive Video (MIV): Bart Kroon, MPEG Video BoG Chair
- Carriage of Versatile Video Coding (VVC) and Enhanced Video Coding (EVC): Young-Kwon Lim, MPEG Systems Chair
- MPEG Roadmap: Jörn Ostermann, Acting Convenor of WG11 (MPEG)
MPEG Web site: https://mpeg-standards.com/meetings/mpeg-131/
EPIQ'21: Days of Future Past: An Optimization-based Adaptive Bitrate Algorith...
HTTP Adaptive Streaming (HAS) has become a predominant technique for delivering videos in the Internet. Due to its adaptive behavior according to changing network conditions, it may result in video quality variations that negatively impact the Quality of Experience (QoE) of the user. In this paper, we propose Days of Future Past, an optimization- based Adaptive Bitrate (ABR) algorithm over HTTP/3. Days of Future Past takes advantage of an optimization model and HTTP/3 features, including (i) stream multiplexing and (ii) request cancellation. We design a Mixed Integer Linear Programming (MILP) model that determines the optimal video qualities of both the next segment to be requested and the segments currently located in the buffer. If better qualities for buffered segments are found, the client will send corresponding HTTP GET requests to retrieve them. Multiple segments (i.e., retransmitted segments) might be downloaded simultaneously to upgrade some buffered but not yet played segments to avoid quality decreases using the stream multiplexing feature of QUIC. HTTP/3’s request cancellation will be used in case retransmitted segments will arrive at the client after their playout time. The experimental results shows that our proposed method is able to improve the QoE by up to 33.9%.
Dynamic Adaptive Point Cloud Streaming
High-quality point clouds have recently gained interest as an emerg- ing form of representing immersive 3D graphics. Unfortunately, these 3D media are bulky and severely bandwidth intensive, which makes it difficult for streaming to resource-limited and mobile de- vices. This has called researchers to propose efficient and adaptive approaches for streaming of high-quality point clouds.
In this paper, we run a pilot study towards dynamic adaptive point cloud streaming, and extend the concept of dynamic adaptive streaming over HTTP (DASH) towards DASH-PC, a dynamic adaptive bandwidth-efficient and view-aware point cloud streaming system. DASH-PC can tackle the huge bandwidth demands of dense point cloud streaming while at the same time can semantically link to human visual acuity to maintain high visual quality when needed. In order to describe the various quality representations, we pro- pose multiple thinning approaches to spatially sub-sample point clouds in the 3D space, and design a DASH Media Presentation Description manifest specific for point cloud streaming. Our initial evaluations show that we can achieve significant bandwidth and performance improvement on dense point cloud streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied.
Quality Optimization of Live Streaming Services over HTTP with Reinforcement ...
Quality Optimization of Live Streaming Services over HTTP with Reinforcement ...Alpen-Adria-Universität
Recent years have seen tremendous growth in HTTP adaptive live video traffic over the Internet. In the presence of highly dynamic network conditions and diverse request patterns, existing yet simple hand-crafted heuristic approaches for serving client requests at the network edge might incur a large overhead and significant increase in time complexity. Therefore, these approaches might fail in delivering acceptable Quality of Experience (QoE) to end users. To bridge this gap, we propose ROPL, a learning-based client request management solution at the edge that leverages the power of the recent breakthroughs in deep reinforcement learning, to serve requests of concurrent users joining various HTTP-based live video channels. ROPL is able to react quickly to any changes in the environment, performing accurate decisions to serve clients requests, which results in achieving satisfactory user QoE. We validate the efficiency of ROPL through trace-driven simulations and a real-world setup. Experimental results from real-world scenarios confirm that ROPL outperforms existing heuristic-based approaches in terms of QoE, with a factor up to 3.7×.Policy-driven Dynamic HTTP Adaptive Streaming Player Environment
Video streaming services account for the majority of today’s traffic on the Internet. Although the data transmission rate has been increasing significantly, the growing number and variety of media and higher quality expectations of users have led networked media applications to fully or even over-utilize the available throughput. HTTP Adaptive Streaming (HAS) has become a predominant technique for multimedia delivery over the Internet today. However, there are critical challenges for multimedia systems, especially the tradeoff between the increasing content (complexity) and various requirements regarding time (latency) and quality (QoE). This thesis will cover the main aspects within the end user’s environment, including video consumption and interactivity, collectively referred to as player environment, which is probably the most crucial component in today’s multimedia applications and services. We will investigate the methods that can enable the specification of various policies reflecting the user’s needs in given use cases. Besides, we will also work on schemes that allow efficient support for server-assisted, and network-assisted HAS systems. Finally, those approaches will be considered to combine into policies that fit the requirements of all use cases (e.g., live streaming, video on demand, etc.).
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video StreamingAlpen-Adria-Universität
Recently, HTTP Adaptive Streaming (HAS) has become the dominant video delivery technology over the Internet. In HAS, clients have full control over the media streaming and adaptation processes. Lack of coordination among the clients and lack of awareness of the network conditions may lead to sub-optimal user experience, and resource utilization in a pure client-based HAS adaptation scheme. Software-Defined Networking (SDN) has recently been considered to enhance the video streaming process. In this paper, we leverage the capability of SDN and Network Function Virtualization (NFV) to introduce an edge- and SDN-assisted video streaming framework called ES-HAS. We employ virtualized edge components to collect HAS clients’ requests and retrieve networking information in a time-slotted manner. These components then perform an optimization model in a time-slotted manner to efficiently serve clients’ requests by selecting an optimal cache server (with the shortest fetch time). In case of a cache miss, a client’s request is served (i) by an optimal replacement quality (only better quality levels with minimum deviation) from a cache server, or (ii) by the originally requested quality level from the origin server. This approach is validated through experiments on a large-scale testbed, and the performance of our framework is compared to pure client-based strategies and the SABR system [11]. Although SABR and ES-HAS show (almost) identical performance in the number of quality switches, ES-HAS outperforms SABR in terms of playback bitrate and the number of stalls by at least 70% and 40%, respectively. Video complexity analyzer (VCA) for streaming applications
Vignesh V Menon and Hadi Amirpour gave a talk on ‘Video Complexity Analyzer for Streaming Applications’ at the Video Quality Experts Group (VQEG) meeting on December 14, 2021. Our research activities on video complexity analysis were presented in the talk.
What's hot (20)
HTTP Adaptive Streaming State of the Art and Challenges Ahead
HTTP Adaptive Streaming State of the Art and Challenges Ahead
CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...
CAdViSE: Cloud based Adaptive Video Streaming Evaluation Framework for the Au...
CAdViSE or how to find the Sweet Spots of ABR Systems
CAdViSE or how to find the Sweet Spots of ABR Systems
Objective and Subjective QoE Evaluation for Adaptive Point Cloud Streaming
Objective and Subjective QoE Evaluation for Adaptive Point Cloud Streaming
Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC...
Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC...
CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming
CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming
WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile D...
WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile D...
EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming
EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming
Towards View-Aware Adaptive Streaming of Holographic Content
Towards View-Aware Adaptive Streaming of Holographic Content
Understanding Quality of Experience of Heuristic-based HTTP Adaptive Bitrate ...
Understanding Quality of Experience of Heuristic-based HTTP Adaptive Bitrate ...
EPIQ'21: Days of Future Past: An Optimization-based Adaptive Bitrate Algorith...
EPIQ'21: Days of Future Past: An Optimization-based Adaptive Bitrate Algorith...
Quality Optimization of Live Streaming Services over HTTP with Reinforcement ...
Quality Optimization of Live Streaming Services over HTTP with Reinforcement ...
Policy-driven Dynamic HTTP Adaptive Streaming Player Environment
Policy-driven Dynamic HTTP Adaptive Streaming Player Environment
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming
ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming
Video complexity analyzer (VCA) for streaming applications
Video complexity analyzer (VCA) for streaming applications
Similar to Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of the Art and Challenges Ahead
HTTP Adaptive Streaming – Quo Vadis? (2023)
Video traffic on the Internet is constantly growing; networked multimedia applications consume a predominant share of the available Internet bandwidth. A major technical breakthrough and enabler in multimedia systems research and of industrial networked multimedia services certainly was the HTTP Adaptive Streaming (HAS) technique. This resulted in the standardization of MPEG Dynamic Adaptive Streaming over HTTP (MPEG-DASH) which, together with HTTP Live Streaming (HLS), is widely used for multimedia delivery in today’s networks. Existing challenges in multimedia systems research deal with the trade-off between (i) the ever-increasing content complexity, (ii) various requirements with respect to time (most importantly, latency), and (iii) quality of experience (QoE). Optimizing towards one aspect usually negatively impacts at least one of the other two aspects if not both. This situation sets the stage for our research work in the ATHENA Christian Doppler (CD) Laboratory (Adaptive Streaming over HTTP and Emerging Networked Multimedia Services; https://athena.itec.aau.at/), jointly funded by public sources and industry. In this talk, we will present selected novel approaches and research results of the first year of the ATHENA CD Lab’s operation. We will highlight HAS-related research on (i) multimedia content provisioning (machine learning for video encoding); (ii) multimedia content delivery (support of edge processing and virtualized network functions for video networking); (iii) multimedia content consumption and end-to-end aspects (player-triggered segment retransmissions to improve video playout quality); and (iv) novel QoE investigations (adaptive point cloud streaming). We will also put the work into the context of international multimedia systems research.
Adaptive Streaming of Traditional and Omnidirectional Media
This tutorial consists of three main parts. In the first part, we provide a detailed overview of the HTML5 standard and show how it can be used for adaptive streaming deployments. In particular, we focus on the HTML5 video, media extensions, and multi-bitrate encoding, encapsulation and encryption workflows, and survey well-established streaming solutions. Furthermore, we present experiences from the existing deployments and the relevant de jure and de facto standards (DASH, HLS, CMAF) in this space. In the second part, we focus on omnidirectional (360) media from creation to consumption. We survey means for the acquisition, projection, coding and packaging of omnidirectional media as well as delivery, decoding and rendering methods. Emerging standards and industry practices are covered as well. The last part presents some of the current research trends, open issues that need further exploration and investigation, and various efforts that are underway in the streaming industry.
How to Optimize Dynamic Adaptive Video Streaming? Challenges and Solutions
Empowered by today’s rich tools for media generation and collaborative production and convenient network access to the Internet, video streaming has become very popular. Dynamic adaptive video streaming is a technique used to deliver video content to users over the Internet, where the quality of the video adapts in real time based on the network conditions and the capabilities of the user’s device. HTTP Adaptive Streaming (HAS) has become the de-facto standard to provide a smooth and uninterrupted viewing experience, especially when network conditions frequently change. Improving the QoE of users concerning various applications‘ requirements presents several challenges, such as network variability, limited resources, and device heterogeneity. For example, the available network bandwidth can vary over time, leading to frequent changes in the video quality. In addition, different users have different preferences and viewing habits, which can further complicate live streaming optimization. Researchers and engineers have developed various approaches to optimize dynamic adaptive streaming, such as QoE-driven adaptation, machine learning-based approaches, and multi-objective optimization, to address these challenges. In this talk, we will give an introduction to the topic of video streaming and point out the significant challenges in the field. We will present a layered architecture for video streaming and then discuss a selection of approaches from our research addressing these challenges. For instance, we will present approaches to improve the QoE of clients in User-generated content applications in centralized and distributed fashions. Moreover, we will present a novel architecture for low-latency live streaming that is agnostic to the protocol and codecs that can work equally with existing HAS-based approaches.
Video Coding Enhancements for HTTP Adaptive Streaming
Vignesh V Menon is invited to talk on "Video Coding for HTTP Adaptive Streaming" on the Research@Lunch, which is a research webinar series by Humanitarian Technology (HuT) Labs, Amrita Vishwa Vidyapeetham University, India, exclusively for Ph.D. Scholars, UG, and PG Researchers in India. This talk will introduce the basics of video codecs and highlight the scope of HAS-related research on video encoding.
Research@Lunch_Presentation.pdf
Invited talk on “Video Coding for HTTP Adaptive Streaming” on the Research@Lunch, which is a research webinar series by Humanitarian Technology (HuT) Labs, Amrita Vishwa Vidyapeetham University, India, exclusively for Ph.D. Scholars, UG, and PG Researchers in India. This talk introduced the basics of video codecs and highlighted the scope of HAS-related research on video encoding.
Time: August 14, 10.00AM-10.30AM (CEST) or 1.30PM- 2.00PM (IST)
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
Video is evolving into a crucial tool as daily lives are increasingly centered around visual communication. The demand for better video content is constantly rising, from entertainment to business meetings. The delivery of video content to users is of utmost significance. HTTP adaptive streaming, in which the video content adjusts to the changing network circumstances, has become the de-facto method for delivering internet video.
As video technology continues to advance, it presents a number of challenges, one of which is the large amount of data required to describe a video accurately. To address this issue, it is necessary to have a powerful video encoding tool. Historically, these efforts have relied on hand-crafted tools and heuristics. However, with the recent advances in machine learning, there has been increasing exploration into using these techniques to enhance video coding performance.
This thesis proposes eight contributions that enhance video coding performance for HTTP adaptive streaming using machine learning.
Zip Mass-An Revolutionary Video Compression Technology
The file size of a 1080P HD movie is only 300M after Zip-Mass compression, of which the original file is usually 26G. It can run fluently by any home computers without special hardware demands.
Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
Thesis Presentation - Ekrem Çetinkaya
Video is evolving into a crucial tool as daily lives are increasingly centered around visual communication. The demand for better video content is constantly rising, from entertainment to business meetings. The delivery of video content to users is of utmost significance. HTTP adaptive streaming, in which the video content adjusts to the changing network circumstances, has become the de-facto method for delivering internet video. As video technology continues to advance, it presents a number of challenges, one of which is the large amount of data required to describe a video accurately. To address this issue, it is necessary to have a powerful video encoding tool. Historically, these efforts have relied on hand-crafted tools and heuristics. However, with the recent advances in machine learning, there has been increasing exploration into using these techniques to enhance video coding performance. This thesis proposes eight contributions that enhance video coding performance for HTTP adaptive streaming using machine learning.
Radvision webinar: Making Real Time Video Work Over The Internet
It's an opportune time for up-and-coming conferencing service providers to deploy collaboration solutions based on VoIP architectures. Early adopters amongst the Conferencing Service Providers (CSPs) have demonstrated the benefits and cost-efficiencies of these technologies in large hosted conferencing deployments. Now is the time for smaller or emerging market CSPs still operating legacy TDM audio bridges to embrace IP-based audio conferencing platforms, and capture your fair share of the growing hosted collaboration market opportunity.
What Attendees will learn:
* Understand the trends and key requirements for business customers using hosted conferencing services, and how traditional TDM audio bridges are not keeping up.
* Learn how IP-based conferencing platforms not only deliver cost-efficiencies in hosted conferencing services, but they also offer the flexibility to seamlessly integrate into collaboration processes and communication behaviours of your target markets.
* Understand the equipment and features required in entry-level systems to get you started, with the scalability to grow, or add video and web collaboration capabilities.
MPEG-DASH open source tools and cloud services
Our presentation from the media web symposium 2013 in Berlin on the open source landscape around MPEG-DASH as well as on cloud-based services for MPEG-DASH
MMSys'21 - Multi-access edge computing for adaptive bitrate video streaming
MMSys'21 - Multi-access edge computing for adaptive bitrate video streaming
Bitmovin LIVE Tech Talks: Analytics for Workflow Automation (ft. Touchstream ...
As part of Bitmovin's NAB 2020 Virtual event series, we were joined by live video monitoring solutions provider Touchstream Media and had the chance to discuss how live-streaming organizations (such as Sports broadcasters) should automate analytics and data to best improve your video workflows.
View our on-demand discussion featuring case studies from a few major sports broadcasters: https://go.bitmovin.com/techtalk-live-analytics-automation-touchstream?utm_source=slideshare
Live, Low Delay, High Quality – How?
Multimedia content delivery and real-time streaming over the top of the existing infrastructure is nowadays part and parcel of every media ecosystem thanks to open standards and the adoption of the Hypertext Transfer Protocol (HTTP) as its primary mean for transportation. Hardware encoder manufacturers have adopted their product lines to support the dynamic adaptive streaming over HTTP but suffer from the inflexibility to provide scalability on demand, specifically for event-based live services that are only offered for a limited period of time. The cloud computing paradigm allows for this kind of flexibility and provide the necessary elasticity in order to easily scale with the demand required for such use case scenarios. In this talk we describe how to deploy a transcoding and streaming-as-a-service platform based on open standards (i.e., mainly MPEG-DASH) utilizing standard cloud and content delivery infrastructures to enable low-delay and high-quality streaming to heterogeneous clients. We describe how to deploy it for video on demand, 24/7 live, and event-based live services. The talk also provides comprehensive evaluation results both with respect the transcoding/streaming and client adaptation behaviour. It allows attendees to identify bottlenecks in their transcoding and streaming workflows and how to use public infrastructure components and MPEG-DASH to overcome existing limitations.
Rebaca's Video Delivery Expertise Overview
Rebaca has strong experience in Video Delivery and Optimization using software and hardware based solutions for Video Headend , IP Video Optimization Appliances and Home Networks.
Following is a brief on our skill set :-
Familiarity with ADM,ADS,CIS,POIS,SIS based on SCTE 130-3-6
Familiarity with variety of Video Containers : FLV , MP4 , FMP4 , AHLS , TS , 3GPP.
Familiarity with wide range of streaming technologies : RTP/RTSP, RTMP, HTTP Progressive streaming, HLS, HDS, Silverlight Smooth Streaming, MPEG-DASH.
Transcoding : FFMPEG , ViXS , Zenverge Transcoders
Caching ,Content Probing
Development of PC/Mobile/Tablet client : Android , iOS , Windows Mobile , Symbian , RIM
Technologies : C,C++,Jave,J2EE,.Net,Python,TCL/TK
Testing and Test Automation for web portals and network devices.
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Multimedia applications, mainly video streaming services, are currently the dominant source of network load worldwide. In recent Video-on-Demand (VoD) and live video streaming services, traditional streaming delivery techniques have been replaced by adaptive solutions based on the HTTP protocol. Current trends toward high-resolution (e.g., 8K) and/or low- latency VoD and live video streaming pose new challenges to end-to-end (E2E) bandwidth demand and have stringent delay requirements. To do this, video providers typically rely on Content Delivery Networks (CDNs) to ensure that they provide scalable video streaming services. To support future streaming scenarios involving millions of users, it is necessary to increase the CDNs’ efficiency. It is widely agreed that these requirements may be satisfied by adopting emerging networking techniques to present Network-Assisted Video Streaming (NAVS) methods. Motivated by this, this thesis goes one step beyond traditional pure client- based HAS algorithms by incorporating (an) in-network component(s) with a broader view of the network to present completely transparent NAVS solutions for HAS clients.Multi-access Edge Computing for Adaptive Video Streaming
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The considerable surge in energy consumption within data centers can be attributed to the exponential rise in demand for complex computing workflows and storage resources. Video streaming applications are both compute and storage-intensive and account for the majority of today’s internet services. In this work, we designed a video encoding application consisting of codec, bitrate, and resolution set for encoding a video segment. Then, we propose VE-Match, a matching-based method to schedule video encoding applications on both Cloud and Edge resources to optimize costs and energy consumption. Evaluation results on a real computing testbed federated between Amazon Web Services (AWS) EC2 Cloud instances and the Alpen-Adria University (AAU) Edge server reveal that VE-Match achieves lower costs by 17%-78% in the cost-optimized scenarios compared to the energy-optimized and tradeoff between cost and energy. Moreover, VE-Match improves the video encoding energy consumption by 38%-45% and gCO2 emission by up to 80 % in the energy-optimized scenarios compared to the cost-optimized and tradeoff between cost and energy.Energy Consumption in Video Streaming: Components, Measurements, and Strategies
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The rapid growth of video streaming usage is a significant source of energy consumption, driven by improved internet connections and service offerings, the quick development of video entertainment, the deployment of Ultra High-Definition, Virtual and Augmented Reality, as well as an increasing number of video surveillance and IoT applications. To address this challenge, it is essential to understand the various components involved in energy consumption during video streaming, ranging from video encoding to decoding and displaying the video on the end user’s screen. Then, it is critical to measure energy consumption for each component accurately and conduct an in-depth analysis to develop energy-efficient strategies that optimize video streaming [1, 2, 3]. These components are classified into three categories [4]: (i) data centers, which include encoding, packaging, and storage on cloud data centers; (ii) networks, which include core network and access networks; and (iii) end-user devices which involve decoding, players, hardware, etc.
In addition to identifying the primary components of video streaming that affect energy consumption, it is important to conduct a comprehensive analysis of the entire video streaming. It is also essential to balance energy optimization and service quality to ensure that energyefficient strategies are implemented without sacrificing the quality of video streaming services.
This talk aims to provide insights into the components of video streaming that contribute to energy consumption and highlight the challenges associated with measuring their energy usage. I will also introduce the tools that can be used for energy measurements for those components and the possible and associated strategies that lie within energy efficiency. By accurately measuring energy consumption, digital media companies can effectively monitor and control their energy usage, ultimately leading to cost savings and improved sustainability.Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
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The rapid growth of video streaming usage is a significant source of energy consumption, driven by improved internet connections and service offerings, the quick development of video entertainment, the deployment of Ultra High-Definition, Virtual and Augmented Reality, as well as an increasing number of video surveillance and IoT applications. However, it is essential to note that these advancements come at the cost of energy consumption. To address this challenge, it is essential to understand the various components involved in energy consumption during video streaming, ranging from video encoding to decoding and displaying the video on the end user’s screen. Then, it is critical to accurately measure energy consumption for each component and conduct an in-depth analysis to develop energy-efficient strategies that optimize video streaming. I categorize these components into three categories: (i) data centers, (ii) networks, and (iii) end-user devices.
In this talk, my objective is to provide insights into the components of video streaming that contribute to energy consumption and highlight the challenges associated with measuring their energy usage. I will also introduce the tools that can be used for energy measurements for those components and the possible and associated strategies that lie within energy efficiency. By accurately measuring energy consumption, digital media companies can effectively monitor and control their energy usage, ultimately leading to cost savings and improved sustainability.Optimizing QoE and Latency of Live Video Streaming Using Edge Computing a...
Nowadays, HTTP Adaptive Streaming (HAS) has become the de-facto standard for delivering video over the Internet. More users have started generating and delivering high-quality live streams (usually 4K resolution) through popular online streaming platforms, resulting in a rise in live streaming traffic. Typically, the video contents are generated by streamers and watched by many audiences, geographically distributed in various locations far away from the streamers. The resource limitation in the network (e.g., bandwidth) is a challenging issue for network and video providers to meet the users’ requested quality. This dissertation leverages edge computing capabilities and in-network intelligence to design, implement, and evaluate approaches to optimize Quality of Experience (QoE) and end-to-end (E2E) latency of live HAS. In addition, improving transcoding performance and optimizing the cost of running live HAS services and the network’s backhaul utilization are considered. Motivated by the mentioned issue, the dissertation proposes five contributions in two classes: optimizing resource utilization and light-weight transcoding.
SARENA: SFC-Enabled Architecture for Adaptive Video Streaming Applications
5G and 6G networks are expected to support various novel emerging adaptive video streaming services (e.g., live, VoD, immersive media, and online gaming) with versatile Quality of Experience (QoE) requirements such as high bitrate, low latency, and sufficient reliability. It is widely agreed that these requirements can be satisfied by adopting emerging networking paradigms like Software-Defined Networking (SDN), Network Function Virtualization (NFV), and edge computing. Previous studies have leveraged these paradigms to present network-assisted video streaming frameworks, but mostly in isolation without devising chains of Virtualized Network Functions (VNFs) that consider the QoE requirements of various types of Multimedia Services (MS). To bridge the aforementioned gaps, we first introduce a set of multimedia VNFs at the edge of an SDN-enabled network, form diverse Service Function Chains (SFCs) based on the QoE requirements of different MS services. We then propose SARENA, an SFC-enabled ArchitectuRe for adaptive VidEo StreamiNg Applications. Next, we formulate the problem as a central scheduling optimization model executed at the SDN controller. We also present a lightweight heuristic solution consisting of two phases that run on the SDN controller and edge servers to alleviate the time complexity of the optimization model in
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- 1. Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of the Art and Challenges Ahead O C T O B E R 2 0 1 8
- 2. 1 Introduction About the Speaker About Bitmovin Motivation HAS – How it works? Software-based Encoding Cloud Encoding Service Managed On-Premise Encoding Challenges Ahead Multi-Bitrate / Multi-Codec Delay / Quality of Experience VR/360 / AI-based HAS 2 5 4 Agenda What about Standards3 Conclusion6
- 3. Associate Professor at the Institute of Information Technology (ITEC), Multimedia Communication Group (MMC), Alpen-Adria- Universität Klagenfurt, Austria; Web: http://itec.aau.at/ Co-founder and CIO | Head of Research and Standardization at Bitmovin; Web: https://bitmovin.com/ Research Interests: immersive multimedia communication, streaming, adaptation, and Quality of experience (QoE) Blog: http://blog.timmerer.com/ Twitter: @timse7 LinkedIn: https://www.linkedin.com/in/christiantimmerer/ SlideShare: https://www.slideshare.net/christian.timmerer About the Speaker 3
- 4. Software to Solve Complex Video Problems https://bitmovin.com/ About Bitmovin 4 Encoding Player Analytics Massively distributed video encoding that runs everywhere Deliver High Quality Video everywhere Control and present your data the way your team needs it
- 5. Bitmovin Solution 5 End to end software and integrations that help our customers deliver cutting edge solutions with more confidence.
- 6. Popular services (global) Netflix (26.58%), HTTP Media Stream (24.40%), YouTube (21.30%), Raw M2TS (8.04%), Amazon Prime (5.73%), Twitch (3.45%); all delivered over-the-top (OTT) Forecast: Visual Networking Index (VNI) 2016-2021 IP video traffic will be 82% of all consumer Internet traffic by 2021 (up from 73% in 2016); will grow threefold from 2016 to 2021 Live Internet video will account for 13% of Internet video traffic by 2021; will grow 15-fold from 2016 to 2021 ______________________________________________ More people now subscribe to Netflix (50.85M) than cable TV (48.61M) in the US (Q1 2017) Motivation 6https://multimediacommunication.blogspot.com/2018/10/almost-58-percent-of-downstream-traffic.html
- 7. HTTP Adaptive Streaming – How it works 7 Adaptation logic is within the client, not normatively specified by the standard, subject to research and development
- 8. Multi-Bitrate Encoding and Representation Switching 8 Contents on the Web Server Request Movie A (200 Kbps) for t=0 Movie A – 200 Kbps Movie A – 400 Kbps Movie A – 1.2 Mbps Movie A – 2.2 Mbps . . . . . . Request Movie A (400 Kbps) for t=16 Request Movie A (800 Kbps) for t=28 Request Manifest for Movie A Movie K – 200 Kbps Movie K – 500 Kbps Movie K – 1.1 Mbps Movie K – 1.8 Mbps . . . . . . Start quickly Keep requesting Improve quality Loss/congestion detection Revamp quality ... . . . Segments Manifest Request Movie A (400 Kbps) for t=2 Request Movie A (800 Kbps) for t=4
- 9. A. Bentaleb, B. Taani, A. C. Begen, C. Timmerer, R. Zimmermann, "A Survey on Bitrate Adaptation Schemes for Streaming Media over HTTP," in IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/COMST.2018.2862938 Bitrate Adaptation Schemes 9 Bitrate Adaptation Schemes Client- based Adaptation Bandwidth- based Buffer- based Mixed adaptation Proprietary solutions MDP-based Server- based Adaptation Network- assisted Adaptation Hybrid Adaptation SDN-based Server and network- assisted
- 10. Adobe: HTTP Dynamic Streaming (HDS); switched to DASH Apple: HTTP Live Streaming (HLS); RFC 8216, required for iOS Microsoft: Smooth Streaming; switched to DASH, almost.. Standards 10 Source: http://xkcd.com/927/ MPEG Dynamic Adaptive Streaming over HTTP (DASH) Supported by Netflix, YouTube, Bitmovin, etc. MPEG Common Media Application Format (CMAF) The new kid on the block – support for “fragmented mp4 in HLS” DASH/HLS convergence at segment level – open issues with encryption format
- 11. Scalability, speed & control Cloud Encoding Service 11
- 12. Cloud Encoding Service Features ○ DRM ○ Live Streaming ○ API Clients ○ Supported Storage ○ Input Formats ○ Output Formats ○ HDR ○ Closed Captions & Subtitles ○ Fully Featured Encoding Service 12
- 14. Massively distributed video encoding that runs anywhere Managed On-Premise Encoding 14
- 15. Managed On-Premise Encoding ○ Hybrid Workflows ○ DRM ○ Live Streaming ○ API Clients ○ Supported Storage ○ Input Formats ○ Output Formats ○ Closed Captions & Subtitles ○ Fully Featured Encoding Service Features 15
- 16. Which video codecs are you currently using? Video Developer Report 2018 16https://bitmovin.com/bitmovin-2018-video-developer-survey-reveals-shifting-technology-landscape/
- 17. Which video codecs are you planning to use in 12 months? Video Developer Report 2018 17https://bitmovin.com/bitmovin-2018-video-developer-survey-reveals-shifting-technology-landscape/
- 18. Multi-bitrate encoding Speed, bitrate/resolution, quality [cf. Per-Title Bitrate Ladder Tool] Multi-codec ecosystem AVC, HEVC, VVC, VP9, AV1, AV2 [cf. WQ.L4: Video Coding at Scale] Delay Identified as the biggest problem for video developers in 2018 (55% globally, 74% in LATAM) Challenges Ahead 18 Quality of Experience (QoE) is the degree of “delight or annoyance of the user” of an application or service [cf. QUALINET] VR/360-degree Video Tile-based streaming, MPEG OMAF [cf. “A Framework for Adaptive Delivery of Omnidirectional Video” at HVEI’18] AI-based HAS (end-to-end) Encoding – Streaming – Analytics
- 19. ○Multimedia Systems Tradeoff [Based on Klara Nahrstedt at IEEE MIPR’18 Retreat] ○Bitmovin now has 100+ employees, 300+ customers worldwide ○https://bitmovin.com/careers Conclusion 19 Quality Content Time Quality of {Content, Service, Experience, Life, …} Content complexity: traditional AV, AR/VR/360, multi-modality/-sensory End-to-end delay, startup delay, channel switching, synchronization, interaction
- 21. References ○ A. Bentaleb, B. Taani, A. C. Begen, C. Timmerer and R. Zimmermann, "A Survey on Bitrate Adaptation Schemes for Streaming Media over HTTP," in IEEE Communications Surveys & Tutorials. https://doi.org/10.1109/COMST.2018.2862938 ○ A. Zabrovskiy, C. Feldmann, C. Timmerer, "Multi-codec DASH dataset," Proc. ACM MMSys'18. https://dx.doi.org/10.1145/3204949.3208140 ○ C. Timmerer, A. C. Begen, "A Framework for Adaptive Delivery of Omnidirectional Video," In Electronic Imaging – Human Vision and Electronic Imaging (HVEI), vol. 2018, no. 16, 2018. http://www.itec.aau.at/bib/files/hvei18-framework-adaptive.pdf ○ M. Graf, C. Timmerer, C. Mueller. "Towards Bandwidth Efficient Adaptive Streaming of Omnidirectional Video over HTTP: Design, Implementation, and Evaluation," Proc. ACM MMSys'17. https://doi.org/10.1145/3083187.3084016 ○ R. Grandl, "Using a Per-Title Bitrate Ladder to Optimize Encoding – Try our new Benchmark Tool," https://bitmovin.com/using-per-title-bitrate-ladder-optimize-encoding-try-new-benchmark-tool/ ○ T. Vernitsky, "Bitmovin 2018 Video Developer Survey," https://bitmovin.com/bitmovin-2018-video- developer-survey-reveals-shifting-technology-landscape/ ○ K. Brunnström, et al., "Qualinet white paper on definitions of quality of experience,” Lausanne, Switzerland, Version 1.2, March 2013. http://www.qualinet.eu