Policy-driven Dynamic HTTP Adaptive Streaming Player Environment
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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.).
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FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...Alpen-Adria-Universität
HTTP Adaptive Streaming(HAS) is the most common approach for delivering video content over the Internet. Therequirement to encode the same content at different quality levels(i.e., representations) in HAS is a challenging problem for content providers. Fast multirate encoding approaches try to accelerate this process by reusing information from previously encoded representations. In this paper, we propose to use convolutional neural networks (CNNs) to speed up the encoding of multiple representations with a specific focus on parallel encoding. In parallel encoding, the overall time-complexity is limited to the maximum time-complexity of one of the representations that are encoded in parallel. Therefore, instead of reducing the time-complexity for all representations, the highest time-complexities are reduced. Experimental results show that FaME-ML achieves significant time-complexity savings in parallel encoding scenarios(41%in average) with a slight increase in bitrate and quality degradation compared to the HEVC reference software.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.
HTTP Adaptive Streaming – Quo Vadis?
This document summarizes an upcoming presentation on HTTP Adaptive Streaming. The presentation will cover content provisioning, delivery, consumption, and end-to-end aspects of HAS, as well as quality of experience. It will introduce ATHENA, a research center focused on adaptive streaming over HTTP and emerging multimedia technologies. The agenda outlines sections on video encoding for HAS, edge computing, network assistance for clients, bitrate adaptation schemes, and quality of experience models. The presenters are Christian Timmerer and Hermann Hellwagner from Alpen-Adria-Universität Klagenfurt.
QoE- and Energy-aware Content Consumption for HTTP Adaptive Streaming
Video streaming services account for the majority of today’s traffic on the Internet, and according to recent studies, this share is expected to continue growing. This implies that many people around the globe utilize video streaming services on a daily basis to fruit video content. Given this broad utilization, research in video streaming is recently moving towards energy-aware approaches, which aim at the minimization of the energy consumption of the devices involved. On the other side, the perception of quality delivered to the user plays an important role, and the advent of HTTP Adaptive Streaming (HAS) changed the way quality is perceived. The focus moved from the Quality of Service (QoS) towards the Quality of Experience (QoE) of the user taking part in the streaming session. Therefore video streaming services need to develop Adaptive BitRate (ABR) techniques to deal with different network environments on the client side or appropriate end-to-end strategies to provide high QoE to the users. The scope of this doctoral study is within the end-to-end environment with a focus on the end-users domain, referred to as the player environment, including video content consumption and interactivity. This thesis aims to investigate and develop different techniques to increase the delivered QoE to the users and reduce the energy consumption of the end devices in HAS context. We present four main research questions to target the related challenges in the domain of content consumption for HAS systems.
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
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LwTE: Light-weight Transcoding at the Edge
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FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...
FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...Alpen-Adria-Universität
HTTP adaptive video streaming is a widespread and sought-after technology on the Internet that allows clients to dynamically switch between different stream qualities presented in the bitrate ladder to optimize overall received video quality. Currently, there exist several approaches of different complexity for building such a ladder. The simplest method is to use a static bitrate ladder, and the more complex one is to compute a per-title encoding ladder. The main drawback of these approaches is that they do not provide bitrate ladders for scenes with different visual complexity within the video. Moreover, most modern methods require additional computationally-intensive test encodings of the entire video to construct the convex hull, used to calculate the bitrate ladder. This paper proposes a new fast per-scene encoding approach called FAUST based on 1) quick entropy-based scene detection and 2) prediction of optimized bitrate ladder for each scene using an artificial neural network. The results show that our model reduces the mean absolute error to 0.15, the mean square error to 0.08, and the bitrate to 13.5% while increasing the difference in video multimethod assessment fusion to 5.6 points.20 Years of Streaming in 20 Minutes
Streaming media has evolved significantly over the past 20 years. Early systems in the 1990s used proprietary protocols over UDP and later included pre-roll buffers and adaptive bitrate techniques. Standards like RTSP, 3GPP, and ISMA provided interoperability but relied on complex server implementations. The shift to HTTP in the 2000s simplified delivery using progressive download and then adaptive streaming formats like HLS, DASH, and CMAF that divide media into short segments. These standards separate the media format from the delivery method, enabling delivery via HTTP while supporting features like DRM and playback across different devices and networks.
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.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).A Distributed Delivery Architecture for User Generated Content Live Streaming...
A Distributed Delivery Architecture for User Generated Content Live Streaming...Alpen-Adria-Universität
Live User Generated Content (UGC) has become very popular in today’s video streaming applications, in particular with gaming and e-sport. However, streaming UGC presents unique challenges for video delivery. When dealing with the technical complexity of managing hundreds or thousands of concurrent streams that are geographically distributed, UGCsystems are forces to made difficult trade-offs with video quality and latency. To bridge this gap, this paper presents a fully distributed architecture for UGC delivery over the Internet, termed QuaLA(joint Quality-Latency Architecture). The proposed architecture aims to jointly optimize video quality and latency for a better user experience and fairness. By using the proximal Jacobi alternating direction method of multipliers(ProxJ-ADMM) technique, QuaLA proposes a fully distributed mechanism to achieve an optimal solution. We demonstrate the effectiveness of the proposed architecture through real-world experiments using the CloudLAB testbed. Experimental results show the outperformance ofQuaLAin achieving high quality with more than 57% improvement while preserving a good level of fairness and respecting a given target latency among all clients compared to conventional client-driven solutionsH2BR: 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%.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.INCEPT: Intra CU Depth Prediction for HEVC
High Efficiency Video Coding (HEVC) improves the encoding efficiency by utilizing sophisticated tools such as flexible Coding Tree Unit (CTU) partitioning. The Coding Unit (CU) can be split recursively into four equally sized CUs ranging from 64×64 to 8×8 pixels. At each depth level (or CU size), intra prediction via exhaustive mode search was exploited in HEVC to improve the encoding efficiency and result in a very high encoding time complexity. This paper proposes an Intra CU Depth Prediction (INCEPT) algorithm, which limits Rate-Distortion Optimization (RDO) for each CTU in HEVC by utilizing the spatial correlation with the neighboring CTUs, which is computed using a DCT energy-based feature. Thus, INCEPT reduces the number of candidate CU sizes required to be considered for each CTU in HEVC intra coding. Experimental results show that the INCEPT algorithm achieves a better trade-off between the encoding efficiency and encoding time saving (i.e., BDR/∆T) than the benchmark algorithms. While BDR/∆T is 12.35% and 9.03% for the benchmark algorithms, it is 5.49% for the proposed algorithm. As a result, INCEPT achieves a 23.34% reduction in encoding time on average while incurring only a 1.67% increase in bit rate than the original coding in the x265 HEVC open-source encoder.
Labmeeting - 20150831 - Overhead and Performance of Low Latency Live Streamin...
This document summarizes research into reducing latency for live video streaming using MPEG-DASH. It introduces MPEG-DASH and how using HTTP chunked transfer encoding and Gradual Decoding Refresh encoding can help lower latency compared to basic DASH. The paper describes experiments conducted to generate and distribute live content using these techniques and evaluate latency, finding they were able to achieve latency as low as 240ms.
Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of th...
Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of th...Alpen-Adria-Universität
ICIP 2018 Innovation Program: Bringing Industry to ICIP: https://2018.ieeeicip.org/InnovationProgram.aspSLFC: Scalable Light Field Coding
Light field imaging enables some post-processing capabilities like refocusing, changing view perspective, and depth estimation. As light field images are represented by multiple views they contain a huge amount of data that makes compression inevitable. Although there are some proposals to efficiently compress light field images, their main focus is on encoding efficiency. However, some important functionalities such as viewpoint and quality scalabilities, random access, and uniform quality distribution have not been addressed adequately. In this paper, an efficient light field image compression method based on a deep neural network is proposed, which classifies multiple views into various layers. In each layer, the target view is synthesized from the available views of previously encoded/decoded layers using a deep neural network. This synthesized view is then used as a virtual reference for the target view inter-coding. In this way, random access to an arbitrary view is provided. Moreover, uniform quality distribution among multiple views is addressed. In higher bitrates where random access to an arbitrary view is more crucial, the required bitrate to access the requested view is minimized.
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.
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. 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 – 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.
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.
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...Alpen-Adria-Universität
HTTP Adaptive Streaming(HAS) is the most common approach for delivering video content over the Internet. Therequirement to encode the same content at different quality levels(i.e., representations) in HAS is a challenging problem for content providers. Fast multirate encoding approaches try to accelerate this process by reusing information from previously encoded representations. In this paper, we propose to use convolutional neural networks (CNNs) to speed up the encoding of multiple representations with a specific focus on parallel encoding. In parallel encoding, the overall time-complexity is limited to the maximum time-complexity of one of the representations that are encoded in parallel. Therefore, instead of reducing the time-complexity for all representations, the highest time-complexities are reduced. Experimental results show that FaME-ML achieves significant time-complexity savings in parallel encoding scenarios(41%in average) with a slight increase in bitrate and quality degradation compared to the HEVC reference software.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.
HTTP Adaptive Streaming – Quo Vadis?
This document summarizes an upcoming presentation on HTTP Adaptive Streaming. The presentation will cover content provisioning, delivery, consumption, and end-to-end aspects of HAS, as well as quality of experience. It will introduce ATHENA, a research center focused on adaptive streaming over HTTP and emerging multimedia technologies. The agenda outlines sections on video encoding for HAS, edge computing, network assistance for clients, bitrate adaptation schemes, and quality of experience models. The presenters are Christian Timmerer and Hermann Hellwagner from Alpen-Adria-Universität Klagenfurt.
What's hot (20)
FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...
FAUST: Fast Per-Scene Encoding Using Entropy-Based Scene Detection and Machin...
CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming
CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming
Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC...
Scalable High Efficiency Video Coding based HTTP Adaptive Streaming over QUIC...
A Distributed Delivery Architecture for User Generated Content Live Streaming...
A Distributed Delivery Architecture for User Generated Content Live 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...
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
Labmeeting - 20150831 - Overhead and Performance of Low Latency Live Streamin...
Labmeeting - 20150831 - Overhead and Performance of Low Latency Live Streamin...
Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of th...
Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of th...
Bandwidth Prediction in Low-Latency Chunked Streaming
Bandwidth Prediction in Low-Latency Chunked Streaming
Understanding Quality of Experience of Heuristic-based HTTP Adaptive Bitrate ...
Understanding Quality of Experience of Heuristic-based HTTP Adaptive Bitrate ...
Video complexity analyzer (VCA) for streaming applications
Video complexity analyzer (VCA) for streaming applications
On Optimizing Resource Utilization in AVC-based Real-time Video Streaming
On Optimizing Resource Utilization in AVC-based Real-time Video Streaming
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...
CAdViSE or how to find the Sweet Spots of ABR Systems
CAdViSE or how to find the Sweet Spots of ABR Systems
Similar to Policy-driven Dynamic HTTP Adaptive Streaming Player Environment
QoE- and Energy-aware Content Consumption for HTTP Adaptive Streaming
Video streaming services account for the majority of today’s traffic on the Internet, and according to recent studies, this share is expected to continue growing. This implies that many people around the globe utilize video streaming services on a daily basis to fruit video content. Given this broad utilization, research in video streaming is recently moving towards energy-aware approaches, which aim at the minimization of the energy consumption of the devices involved. On the other side, the perception of quality delivered to the user plays an important role, and the advent of HTTP Adaptive Streaming (HAS) changed the way quality is perceived. The focus moved from the Quality of Service (QoS) towards the Quality of Experience (QoE) of the user taking part in the streaming session. Therefore video streaming services need to develop Adaptive BitRate (ABR) techniques to deal with different network environments on the client side or appropriate end-to-end strategies to provide high QoE to the users. The scope of this doctoral study is within the end-to-end environment with a focus on the end-users domain, referred to as the player environment, including video content consumption and interactivity. This thesis aims to investigate and develop different techniques to increase the delivered QoE to the users and reduce the energy consumption of the end devices in HAS context. We present four main research questions to target the related challenges in the domain of content consumption for HAS systems.
Video Streaming: Then, Now, and in the Future
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Multi-access Edge Computing for Adaptive Video Streaming
The document discusses research on using multi-access edge computing (MEC) to improve adaptive video streaming. It presents several contributions, including developing a MEC and HAS simulator called ANGELA, proposing dynamic segment repackaging at the edge to increase bandwidth savings, and designing edge-assisted adaptation schemes (EADAS and ECAS-ML) that leverage edge resources to guide client-based ABR algorithms and improve QoE and fairness. It also investigates segment prefetching policies at the edge such as ones based on last quality, Markov models, transrating, machine learning, and super-resolution.
QoE- and Energy-aware Content Consumption for HTTP Adaptive Streaming - Poster
Video streaming services account for the majority of today’s traffic on the Internet, and according to recent studies, this share is expected to continue growing. This implies that many people around the globe utilize video streaming services on a daily basis to fruit video content. Given this broad utilization, research in video streaming is recently moving towards energy-aware approaches, which aim at the minimization of the energy consumption of the devices involved. On the other side, the perception of quality delivered to the user plays an important role, and the advent of HTTP Adaptive Streaming (HAS) changed the way quality is perceived. The focus moved from the Quality of Service (QoS) towards the Quality of Experience (QoE) of the user taking part in the streaming session. Therefore video streaming services need to develop Adaptive BitRate (ABR) techniques to deal with different network environments on the client side or appropriate end-to-end strategies to provide high QoE to the users. The scope of this doctoral study is within the end-to-end environment with a focus on the end-users domain, referred to as the player environment, including video content consumption and interactivity. This thesis aims to investigate and develop different techniques to increase the delivered QoE to the users and reduce the energy consumption of the end devices in HAS context. We present four main research questions to target the related challenges in the domain of content consumption for HAS systems.
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.
A brief history of streaming video in the Internet
The document provides a history of streaming video over the internet from the 1970s to present. It discusses early challenges with limited network capacity and the investigation of technologies like ATM. Improvements in codecs occurred in the 1990s while applications like Skype emerged in the 2000s. The development of dynamic adaptive streaming over HTTP (DASH) in the 2010s allowed clients to choose video quality based on network conditions over HTTP. Ongoing research focuses on improving adaptation logic and addressing new scenarios like 5G networks. Control theoretic approaches have been applied to buffer stabilization and playout smoothness.
MHV'22 - Super-resolution Based Bitrate Adaptation for HTTP Adaptive Streamin...
This document proposes a super-resolution (SR) based approach for HTTP adaptive streaming on mobile devices. It introduces an SR neural network called SR-ABR Net that can run in real-time on mobile GPUs while providing quality comparable to state-of-the-art SR models. It also presents a SR-aware adaptive bitrate (ABR) algorithm called WISH-SR that leverages SR-ABR Net to improve video quality while reducing data usage. The evaluation shows WISH-SR achieves a 43% reduction in data usage compared to other ABR methods while maintaining a high quality of experience score.
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)
Immersive Video Delivery: From Omnidirectional Video to Holography
Video services are evolving from traditional two-dimensional video to virtual reality and holograms, which offer six degrees of freedom to users, enabling them to freely move around in a scene and change focus as desired. However, this increase in freedom translates into stringent requirements in terms of ultra-high bandwidth (in the order of Gigabits per second) and minimal latency (in the order of milliseconds). To realize such immersive services, the network transport, as well as the video representation and encoding, have to be fundamentally enhanced. The purpose of this tutorial article is to provide an elaborate introduction to the creation, streaming, and evaluation of immersive video. Moreover, it aims to provide lessons learned and to point at promising research paths to enable truly interactive immersive video applications toward holography.
Online Bitrate ladder prediction for Adaptive VVC Streaming
Slides of my talk, "Online Bitrate ladder prediction for Adaptive VVC Streaming," at SEGMENTS:2024 organized by SVTA.
MMSys'21 - Multi-access edge computing for adaptive bitrate video streaming
This document presents research on using multi-access edge computing (MEC) to improve adaptive bitrate video streaming. The research questions examine how MEC can provide network assistance for HTTP adaptive streaming, use radio and client context data to coordinate streaming, enable low-latency predictions, and allow edge node collaboration. The methodology will develop concepts, implement prototypes, and conduct quantitative analysis. Ongoing work includes using common media format to reduce storage needs and an approach that improves adaptation decisions using edge awareness. Future work predicts requests using machine learning and establishes inter-edge node communication for caching.
MMSys'21 DS- RezaFarahani.pdf
1) The document discusses research into improving HTTP adaptive video streaming through network assistance from CDNs and SDNs.
2) It poses research questions about how CDNs and SDNs can provide assistance to HAS clients to improve delivery, and whether client assistance to networks could also be beneficial.
3) The state of the art involves standards for networks to provide information to clients, as well as some research on network-assisted HAS using traditional and SDN-enabled network architectures.
Overview of Selected Current MPEG Activities
The document provides an overview of selected current activities within MPEG, including requirements and timelines. It discusses the Mobile Visual Search work item which aims to enable efficient transmission of local image features for mobile visual search applications. It also outlines the MPEG Media Transport work item which focuses on efficient delivery of media to enable content and network adaptive streaming. Additionally, it summarizes the Advanced IPTV Terminal work item and its goal of defining elementary services and protocols to enable interoperability.
Overview of Selected Current MPEG Activities
The document provides an overview of selected current activities within MPEG, including requirements and timelines. It discusses the Mobile Visual Search work item which aims to enable efficient transmission of local image features for mobile visual search applications. It also outlines the MPEG Media Transport work item which focuses on efficient delivery of media to enable content and network adaptive streaming. Additionally, it summarizes the Advanced IPTV Terminal work item and its goal of defining elementary services and protocols to enable interoperability.
On MPEG Modern Transport over Network
This document discusses MPEG Modern Transport over Networks and addresses three key areas: 1) Developing a transport and file format friendly stream format for peer-to-peer video streaming, 2) Enabling interoperability for cross-layer optimizations between video and transport layers, and 3) Advancing context- and content-aware networks. The document outlines challenges and opportunities for MPEG standards to support new streaming and delivery mechanisms like P2P and address the blurring line between coding and transport layers with developments like scalable video coding and media-aware networks.
Doctoral Symposium presentation.pdf
Abstract: Rapid growth in multimedia streaming traffic over the Internet motivates the research and further investigation of the video coding performance of such services in terms of speed and Quality of Experience (QoE). HTTP Adaptive Streaming (HAS) is today’s de-facto standard to deliver clients the highest possible video quality. In HAS, the same video content is encoded at multiple bitrates, resolutions, framerates, and coding formats called representations. This study aims to (i) provide fast and compression-efficient multi-bitrate, multi-resolution representations, (ii) provide fast and compression-efficient multi-codec representations, (iii) improve the encoding efficiency of Video on Demand (VoD) streaming using content-adaptive encoding optimizations, and (iv) provide encoding schemes with optimizations per-title for live streaming applications to decrease the storage or delivery costs or/and increase QoE.
Hossfeld qc man2015_context_monitoring_web
This document discusses context monitoring and its potential to improve quality of experience (QoE) for HTTP adaptive streaming. It defines context as any information that helps determine a user, network, or device situation. Context influence factors include physical, temporal, social, economic, task, and technical characteristics. The document presents a case study on using context monitoring to improve QoE during video flash crowds. It describes a simulation model of an HTTP adaptive streaming system with two content delivery networks and proposes using context data on the number of users per network to improve load balancing during flash crowds. The results show context monitoring can significantly improve performance and QoE compared to approaches without this context data.
Research Group Multimedia Communication (MMC)
Research Group Multimedia Communication (MMC), Department of Information Technology (ITEC)
Klagenfurt University (UNIKLU), Austria
Towards User-centric Video Transmission in Next Generation Mobile Networks
Towards User-centric Video Transmission in Next Generation Mobile NetworksFörderverein Technische Fakultät
There is a massive growth in mobile video consumption which outpaces the capacity improvements in next generation mobile networks. Specifically, mobile network operators face the challenge of allocating the scarce wireless resources while maximizing the user quality of experience (QoE). The first part of this talk addresses the main challenges in uplink distribution of user-generated video content over fourth generation mobile networks. The second part explores the benefit of QoE-based traffic and resource management in the mobile network in the context of adaptive HTTP downlink video delivery.Similar to Policy-driven Dynamic HTTP Adaptive Streaming Player Environment (20)
QoE- and Energy-aware Content Consumption for HTTP Adaptive Streaming
QoE- and Energy-aware Content Consumption for HTTP Adaptive Streaming
Multi-access Edge Computing for Adaptive Video Streaming
Multi-access Edge Computing for Adaptive Video Streaming
QoE- and Energy-aware Content Consumption for HTTP Adaptive Streaming - Poster
QoE- and Energy-aware Content Consumption for HTTP Adaptive Streaming - Poster
A brief history of streaming video in the Internet
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MHV'22 - Super-resolution Based Bitrate Adaptation for HTTP Adaptive Streamin...
MHV'22 - Super-resolution Based Bitrate Adaptation for HTTP Adaptive Streamin...
Video Coding Enhancements for HTTP Adaptive Streaming
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Immersive Video Delivery: From Omnidirectional Video to Holography
Immersive Video Delivery: From Omnidirectional Video to Holography
Online Bitrate ladder prediction for Adaptive VVC Streaming
Online Bitrate ladder prediction for Adaptive VVC Streaming
MMSys'21 - Multi-access edge computing for adaptive bitrate video streaming
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Towards User-centric Video Transmission in Next Generation Mobile Networks
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More from Minh Nguyen
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
In the last decades, video streaming has been developing significantly. Among current technologies, HTTP Adaptive Streaming (HAS) is considered the de-facto approach in multimedia transmission over the internet. Though the majority of HAS-based media services function well even under throughput restrictions and variations, there are still significant challenges for multimedia systems, especially the tradeoff among the increasing content complexity, various time-related requirements, and Quality of Experience (QoE). Optimizing for one aspect usually negatively impacts at least one of the other two aspects. This thesis tackles critical open research questions in the context of HAS that significantly impact the QoE at the client side. The main contributions of this thesis are four-fold:
- We propose Days of Future Past Plus (DoFP+) approach that leverages HTTP/3’s features to upgrade low-quality segments while downloading others.
- This thesis proposes a weighted sum model, namely WISH, to provide a high QoE of the video and allow end users to express their preferences among different parameters, including data usage, stall events, and video quality.
- To improve segment qualities on high-end mobile devices, this thesis introduces an ABR scheme called WISH-SR that integrates a lightweight Convolutional Neural Network (CNN) to enhance low-resolution/low-quality videos at the client side.
- To improve segment qualities on high-end mobile devices, this thesis introduces an ABR scheme called WISH-SR that integrates a lightweight Convolutional Neural Network (CNN) to enhance low-resolution/low-quality videos at the client side.
CADLAD: Device-aware Bitrate Ladder Construction for HTTP Adaptive Streaming
Considering network conditions, video content, and viewer device type/screen resolution to construct a bitrate ladder is necessary to deliver the best Quality of Experience (QoE). A large-screen device like a TV needs a high bitrate with high resolution to provide good visual quality, whereas a small one like a phone requires a low bitrate with low resolution. In addition, encoding high-quality levels at the server side while the network is unable to deliver them causes unnecessary cost for the content provider. Recently, the Common Media Client Data (CMCD) standard has been proposed, which defines the data that is collected at the client and sent to the server with its HTTP requests. This data is useful in log analysis, quality of service/experience monitoring and delivery improvements.
In this paper, we introduce a CMCD-Aware per-Device bitrate LADder construction (CADLAD) that leverages CMCD to address the above issues. CADLAD comprises components at both client and server sides. The client calculates the top bitrate (tb) — a CMCD parameter to indicate the highest bitrate that can be rendered at the client — and sends it to the server together with its device type and screen resolution. The server decides on a suitable bitrate ladder, whose maximum bitrate and resolution are based on CMCD parameters, to the client device with the purpose of providing maximum QoE while minimizing delivered data. CADLAD has two versions to work in Video on
Demand (VoD) and live streaming scenarios. Our CADLAD is client agnostic; hence, it can work with any players and ABR algorithms at the client. The experimental results show that CADLAD is able to increase the QoE by 2.6x while saving 71% of delivered data, compared to an existing bitrate ladder of an available video dataset. We implement our idea within CAdViSE — an open-source testbed for reproducibility.
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 Dynamic Adaptive Streaming over HTTP (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 from an End-to-end or holistic perspective [1].
CAdViSE provides a Cloud-based Adaptive Video Streaming Evaluation framework for the automated testing of adaptive media players [4]. We 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 we will exhibit its capabilities when combined with objective Quality of Experience (QoE) models. Our team at Bitmovin Inc. and ATHENA laboratory has selected the ITU-T P.1203 (mode 1) quality evaluation model in order to assess the 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 [2]. We 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 our 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 experimental session. These logs can then be applied against different goals, such as objective evaluation or to stitch back media segments and conduct subjective evaluations. In addition, startup delays, stall events, and other media streaming defects can be imitated exactly as they happened during the experimental streaming sessions [3].
Video streaming using light-weight transcoding and in-network intelligence
In this paper, we introduce a novel approach, LwTE, which reduces streaming costs in HTTP Adaptive Streaming (HAS) by enabling light-weight transcoding at the edge. In LwTE, during encoding of a video segment in the origin server, a metadata is generated which stores the optimal encoding decisions. LwTE enables us to store only the highest bitrate plus corresponding metadata (of very small size) for unpopular video segments/bitrates. Since metadata is of very small size, replacing unpopular video segments/bitrates with their metadata results in considerable saving in the storage costs. The metadata is reused at the edge servers to reduce the required time and computational resources for on-the-fly transcoding.
Efficient bitrate ladder construction for live video streaming
In live streaming applications, service providers generally use a bitrate ladder with fixed bitrate-resolution pairs instead of optimizing it per title to avoid the additional latency caused to find optimum bitrate-resolution pairs for every video content. This paper introduces an online bitrate ladder construction scheme for live video streaming applications. In this scheme, each target bitrate's optimized resolution is determined from any pre-defined set of resolutions using Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features for each video segment. Experimental results show that, on average, the proposed scheme yields significant bitrate savings while maintaining the same quality, compared to the HLS fixed bitrate ladder scheme without any noticeable additional latency in streaming.
RICHTER: hybrid P2P-CDN architecture for low latency live video streaming
Content Distribution Networks (CDN) and HTTP Adaptive Streaming (HAS) are considered the principal video delivery technologies over the Internet. Despite the wide usage of these technologies, designing cost-effective, scalable, and flexible architectures that support low latency and high quality live video streaming is still a challenge. To address this issue, we leverage existing works that have combined the characteristics of Peer-to-Peer (P2P) networks and CDN-based systems and introduce a hybrid CDN-P2P live streaming architecture. When dealing with the technical complexity of managing hundreds or thousands of concurrent streams, such hybrid systems can provide low latency and high quality streams by enabling the delivery architecture to switch between the CDN and the P2P modes. However, modern networking paradigms such as Edge Computing, Network Function Virtualization (NFV), and distributed video transcoding have not been extensively employed to design hybrid P2P-CDN streaming systems. To bridge the aforementioned gaps, we introduce a hybRId P2P-CDN arcHiTecture for low LatEncy live video stReaming (RICHTER), discuss the details of its design, and finally give a few directions of the future work.
MHV'22 - Take the Red Pill for H3 and See How Deep the Rabbit Hole Goes
With the introduction of HTTP/3 (H3) and QUIC at its core, there is an expectation of significant improvements in Web-based secure object delivery. As HTTP is a central protocol to the current adaptive streaming methods in all major over-the-top (OTT) services, an important question is what H3 will bring to the table for such services. To answer this question, we present the new features of H3 and QUIC, and compare them to those of H/1.1/2 and TCP. We also share the latest research findings in this domain.
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Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
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CADLAD: Device-aware Bitrate Ladder Construction for HTTP Adaptive Streaming
CADLAD: Device-aware Bitrate Ladder Construction for HTTP Adaptive Streaming
CAdViSE or how to find the sweet spots of ABR systems
CAdViSE or how to find the sweet spots of ABR systems
Video streaming using light-weight transcoding and in-network intelligence
Video streaming using light-weight transcoding and in-network intelligence
Efficient bitrate ladder construction for live video streaming
Efficient bitrate ladder construction for live video streaming
RICHTER: hybrid P2P-CDN architecture for low latency live video streaming
RICHTER: hybrid P2P-CDN architecture for low latency live video streaming
MHV'22 - Take the Red Pill for H3 and See How Deep the Rabbit Hole Goes
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Policy-driven Dynamic HTTP Adaptive Streaming Player Environment
- 1. Policy-driven Dynamic HTTP Adaptive Streaming Player Environment ACM MMSys’21 Doctoral Symposium September 30, 2021 Minh Nguyen Christian Doppler laboratory ATHENA | Alpen-Adria-Universität Klagenfurt | Austria minh.nguyen@aau.at | https://athena.itec.aau.at 1
- 2. ● Introduction ● State of The Art ● Research Questions ● Methodology ● Publications and Future Work Agenda 2
- 4. HTTP Adaptive Streaming (HAS) 4 Video segmentation Video encoding ... ... ... Server Client/ HAS player Adaptive bitrate algorithm Throughput estimation Playout buffer Video decoding ... HTTP GET requests Version 3 Version 2 Version 1 Internet Throughput Time
- 5. Main Target 5 Quality Time Content End-to-end delay, startup delay,... Traditional video, Virtual reality video,... Quality of {Content, Experience,...} Addressing the tradeoff among Quality, Content, and Time
- 6. 6 State of The Art
- 7. Client side 7 Client Adaptive bitrate algorithm Bentaleb, et al. "A survey on bitrate adaptation schemes for streaming media over HTTP." IEEE Communications Surveys & Tutorials 21.1 (2018): 562-585. Markov Decision Process based S2 S0 S1 Artificial Intelligence based AI Hybrid Throughput based Buffer based
- 8. Emerging and future networking approaches 8 Internet ... HTTP/2 HTTP/3 SDN 5G TCP TCP Multipath TCP
- 10. Research questions 10 ● RQ1: How to provide a generic mechanism for HAS players that meets customer needs? ○ Tradeoff: content, quality, and time for each use case (e.g., live streaming, and video on demand). ● RQ2: How to enable efficient support for server-/network assisted HAS? ○ Sharing server-/network information to/among HAS players to ensure quality fairness among multiple clients.
- 11. Research questions 11 ● RQ3: How to add support for emerging/future networking aspects and paradigms? ○ Utilizing their features to improve HAS player’s performance. ● RQ4: How to enable client-based quality enhancement options for HAS players? ○ Applying Deep Neural Networks such as super-resolution ones to avoid the dependence on the network condition. ● RQ5: How to integrate advanced analytics options and various predictions models for HAS players? ○ Utilizing machine learning-based methods such as Long Short Term Memory.
- 12. 12 Methodology
- 13. Methodology 13 Analyze user needs. Research state-of- the-art. State requirements Describe behaviors and technical characteristics. State specifications Design and implement possible solutions. Design and implement Conduct extensive experiments and make comparison. Test Follow design paradigm introduced by Association for Computing Machinery (ACM) [1]. [1] D. E. Comer, David Gries, Michael C. Mulder, Allen Tucker, A. Joe Turner, and Paul R. Young. Computing as a discipline. Communications of the ACM, 32(1):9– 23, January 1989
- 14. 14 Publications and Future Work
- 15. H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive Video Streaming 15 ● Research goals: ○ Improving low-quality segments ○ Filling the quality gaps Nguyen, M., Timmerer, C. and Hellwagner, H., 2020, June. H2BR: An HTTP/2-based retransmission technique to improve the QoE of adaptive video streaming. In Proceedings of the 25th ACM Workshop on Packet Video, pages 1-7, 2020.
- 16. H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive Video Streaming 16 ● Our approach ○ Retransmitting downloaded segments with higher quality versions. ○ Utilizing HTTP/2 features (multiplexing, server push, stream priority, termination). ● Experimental results ○ - 70% lowest-quality segments ○ + 13% QoE Nguyen, M., Timmerer, C. and Hellwagner, H., 2020, June. H2BR: An HTTP/2-based retransmission technique to improve the QoE of adaptive video streaming. In Proceedings of the 25th ACM Workshop on Packet Video, pages 1-7, 2020.
- 17. WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices 17 ● Observation Nguyen, M., Cetinkaya, E., Hellwagner, H. and Timmerer, C., WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices. In 2021 IEEE 23rd Int’l. Workshop on Multimedia Signal Processing (MMSP). IEEE, 2021 More transferred data (high data cost) More download time (high buffer cost) Higher quality (less quality cost) Download high-bitrate segment
- 18. WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices 18 ● Idea ○ The overall cost of each representation is the weighted sum of Throughput cost, Buffer cost, and Quality cost ○ Lowest overall cost representation is selected. ● Results ○ - 36% data usage ○ + 18% QoE Nguyen, M., Cetinkaya, E., Hellwagner, H. and Timmerer, C., WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices. In 2021 IEEE 23rd Int’l. Workshop on Multimedia Signal Processing (MMSP). IEEE, 2021
- 19. Ongoing and Future Work 19 ● RQ 2 ○ Define a notion of QoE fairness ○ Design optimization models with the input as server-/network information to maximize QoE fairness ● RQ 4 ○ Apply Deep Neural Networks such as super-resolution to improve the QoE. ● RQ 5 ○ Work on QoE prediction models ○ Investigate appropriate client metrics to improve QoE
- 20. Thank you 20