WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices
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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.
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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.aspQuality impact of scalable video coding tunneling for media aware content del...
Quality impact of scalable video coding tunneling for media aware content del...Alpen-Adria-Universität
The document discusses a study on using Scalable Video Coding (SVC) tunneling for media-aware content delivery. SVC can save bandwidth for multicast content delivery but many devices do not support SVC natively. The study proposes using SVC tunneling only for delivery, with transcoding at ingress and egress points. The study tests transcoding MPEG-2 video to SVC and back to MPEG-2, finding a peak signal-to-noise ratio drop of 2.1 dB and 43% lower bandwidth requirements compared to MPEG-2 simulcast. Future work could analyze different video formats and implementations of SVC.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.
Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
In the last decades, video streaming has been developing significantly. Among cur- rent technologies, HTTP Adaptive Streaming (HAS) is considered the de-facto approach in multimedia transmission over the internet. In HAS, the video is split into temporal segments with the same duration (e.g., 4s), each of which is then encoded into different quality versions and stored at servers. The end user sends requests to the server to retrieve segments with specific quality versions determined by an Adaptive Bitrate (ABR) algorithm for the purpose of adapting the throughput fluctuation. 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). Content complexity encompasses the increased demands for data, such as high-resolution videos and high frame rates, as well as novel content formats, such as virtual reality (VR) and augmented reality (AR). Time-related requirements include – but are not limited to – start-up delay and end-to-end latency. QoE can be defined as the level of satisfaction or frustration experienced by the user of an application or service. 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.
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.
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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.
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×.CSDN: CDN-Aware QoE Optimization in SDN-Assisted HTTP Adaptive Video Streaming
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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.ES-HAS: An Edge- and SDN-Assisted Framework for HTTP Adaptive Video Streaming
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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.EADAS: Edge Assisted Adaptation Scheme for HTTP Adaptive Streaming
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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. Docker-Based Evaluation Framework for Video Streaming QoE in Broadband Networks
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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%.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.
On the Impact of Viewing Distance on Perceived Video Quality
Due to the growing importance of optimizing quality and efficiency of video streaming delivery, accurate assessment of user perceived video quality becomes increasingly relevant. However, due to the wide range of viewing distances encountered in real-world viewing settings, actually perceived video quality can vary significantly in everyday viewing situations. In this paper, we investigate and quantify the influence of viewing distance on perceived video quality. A subjective experiment was conducted with full HD sequences at three different stationary viewing distances, with each video sequence being encoded at three different quality levels. Our study results confirm that the viewing distance has a significant influence on the quality assessment. In particular, they show that an increased viewing distance generally leads to an increased perceived video quality, especially at low media encoding quality levels. In this context, we also provide an estimation of potential bitrate savings that knowledge of actual viewing distance would enable in practice.
Since current objective video quality metrics do not systematically take into account viewing distance, we also analyze and quantify the influence of viewing distance on the correlation between objective and subjective metrics. Our results confirm the need for distance-aware objective metrics when accurate prediction of perceived video quality in real-world environments is required.
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.
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.
Press Release of 131st WG11 (MPEG) Meeting
The 131st WG 11 (MPEG) meeting was held online, 29 June – 3 July 2020
Table of Contents
WG11 (MPEG) Announces VVC – the Versatile Video Coding Standard
Point Cloud Compression – WG11 (MPEG) promotes a Video-based Point Cloud Compression Technology to the FDIS stage
MPEG-H 3D Audio – WG11 (MPEG) promotes Baseline Profile for 3D Audio to final stage
Call for Proposals on Technologies for MPEG-21 Contracts to Smart Contracts Conversion
WG11 (MPEG) issues a Call for Proposals on extension and improvements to ISO/IEC 23092 standard series
Widening support for storage and delivery of MPEG-5 EVC
Multi-Image Application Format adds support of HDR
Carriage of Geometry-based Point Cloud Data progresses to Committee Draft
MPEG Immersive Video (MIV) progresses to Committee Draft
Neural Network Compression for Multimedia Applications – WG11 (MPEG) progresses to Committee Draft
WG11 (MPEG) issues Committee Draft of Conformance and Reference Software for Essential Video Coding (EVC)
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
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The document discusses a study on using Scalable Video Coding (SVC) tunneling for media-aware content delivery. SVC can save bandwidth for multicast content delivery but many devices do not support SVC natively. The study proposes using SVC tunneling only for delivery, with transcoding at ingress and egress points. The study tests transcoding MPEG-2 video to SVC and back to MPEG-2, finding a peak signal-to-noise ratio drop of 2.1 dB and 43% lower bandwidth requirements compared to MPEG-2 simulcast. Future work could analyze different video formats and implementations of SVC.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.
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Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
In the last decades, video streaming has been developing significantly. Among cur- rent technologies, HTTP Adaptive Streaming (HAS) is considered the de-facto approach in multimedia transmission over the internet. In HAS, the video is split into temporal segments with the same duration (e.g., 4s), each of which is then encoded into different quality versions and stored at servers. The end user sends requests to the server to retrieve segments with specific quality versions determined by an Adaptive Bitrate (ABR) algorithm for the purpose of adapting the throughput fluctuation. 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). Content complexity encompasses the increased demands for data, such as high-resolution videos and high frame rates, as well as novel content formats, such as virtual reality (VR) and augmented reality (AR). Time-related requirements include – but are not limited to – start-up delay and end-to-end latency. QoE can be defined as the level of satisfaction or frustration experienced by the user of an application or service. 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.
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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.
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IEEEGlobecom'22-OL-RICHTER.pdf
This document proposes a hybrid P2P-CDN architecture called RICHTER for live video streaming. RICHTER leverages NFV and edge computing to employ virtual transcoding servers that optimize content delivery by intelligently selecting whether to fetch or transcode content from peers, CDNs or the origin server. An online learning approach is used to solve the NP-hard optimization problem. Evaluation on a large-scale testbed shows RICHTER improves QoE, latency and network utilization compared to baseline schemes. Future work includes extending the action classification tree.
IEEE ICC'22_ LEADER_ A Collaborative Edge- and SDN-Assisted Framework for HTT...
1) The document proposes LEADER, a collaborative edge- and SDN-assisted framework for HTTP adaptive video streaming. LEADER employs virtual network functions with transcoding capabilities at network edges to optimize video streaming quality of experience and network utilization.
2) An SDN controller runs an optimization model to determine the optimal location, action, and approach for fetching client-requested video qualities. A lightweight heuristic approach is also proposed.
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1) The document presents CSDN, a framework that leverages SDN and NFV to provide network assistance for HTTP adaptive video streaming. It proposes using SDN virtual routers equipped with transcoding capabilities to optimize quality of experience (QoE) based on network conditions and user preferences.
2) An evaluation of CSDN on a testbed with 100 clients showed it improved playback bitrate by 7.5% and reduced quality switches and stalls by 19% compared to state-of-the-art approaches, enhancing user QoE and network utilization.
3) Future work directions include improving edge caching strategies, developing learning-based approaches, and extending an MILP model to optimize transcoding
ACM NOSSDAV'21-ES-HAS_ An Edge- and SDN-Assisted Framework for HTTP Adaptive ...
The document presents ES-HAS, an edge- and SDN-assisted framework for HTTP adaptive video streaming. ES-HAS leverages SDN and NFV paradigms to provide network assistance for video streaming. It introduces virtual reverse proxy servers at the network edge that employ a novel server/segment selection policy. An evaluation on a large-scale cloud testbed with 60 clients shows that ES-HAS outperforms state-of-the-art approaches in terms of playback bitrate and number of stalls by at least 70% and 40% respectively. Future work directions include extending edge caching and collaboration as well as improving the proposed optimization model.
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1) The document proposes an end-to-end quality of experience (QoE) optimization architecture using overlay routing in the network core and bandwidth management and transcoding at the edge to optimize user experience of multimedia services.
2) The architecture includes overlay servers, overlay access components, and network intelligence proxies which work together for resilient routing, last mile QoE optimization through traffic shaping and multimedia services, and dynamic H.264 video transcoding.
3) An experiment on the testbed showed the architecture was able to respect last mile bandwidth constraints, distribute bandwidth optimally based on stream importance, and fully utilize available bandwidth through continuous video adaptation, improving user QoE.
(Slides) P2P video broadcast based on per-peer transcoding and its evaluatio...
Shibata, N., Yasumoto, K., and Mori, M.: P2P Video Broadcast based on Per-Peer Transcoding and its Evaluation on PlanetLab, Proc. of 19th IASTED Int'l. Conf. on Parallel and Distributed Computing and Systems (PDCS2007), (November 2007).
http://ito-lab.naist.jp/themes/pdffiles/071121.shibata.pdcs2007.pdf
LwTE-Live: Light-weight Transcoding at the Edge for Live Streaming
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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.
A Distributed Approach for Bitrate Selection in HTTP Adaptive Streaming
A Distributed Approach for Bitrate Selection in HTTP Adaptive StreamingFörderverein Technische Fakultät
Past research has shown that concurrent HTTP adaptive streaming (HAS) players behave selfishly and the resulting competition for shared resources leads to underutilization or oversubscription of the network, presentation quality instability and unfairness among the players, all of which adversely impact the viewer experience. While coordination among the players, as opposed to all being selfish, has its merits and may alleviate some of these issues. A fully distributed architecture is still desirable in many deployments and better reflects the design spirit of HAS. In this study, we focus on and propose a distributed bitrate adaptation scheme for HAS that borrows ideas from consensus and game theory frameworks. Experimental results show that the proposed distributed approach provides significant improvements in terms of viewer experience, presentation quality stability, fairness and network utilization, without using any explicit communication between the players.Labmeeting - 20151013 - Adaptive Video Streaming over HTTP with Dynamic Resou...
Truong Cong Thang Univ. of Aizu, Aizu-Wakamatsu, Japan
Le, H.T. ; Nguyen, H.X. ; Pham, A.T. ; Jung Won Kang ; Yong Man Ro
Journal of Communications and Networks, Vol. 15, No. 6, Dec. 2013
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.
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.
A QoS-Adaptive Framework for Screen Sharing Over Internet
This is the slides of my presentation at 2016 IEEE International Conference on Ubiquitous and Future Networks, Vienna, Austria.
Mini proj ii sdn video communication
The document discusses using software-defined networking (SDN) to improve quality of experience (QoE) for video streaming applications. It presents three research scenarios:
1) An SDN testbed that routes YouTube video traffic to minimize buffering times and maintain high video quality. Application-aware path selection achieved full bandwidth utilization while keeping buffer levels high.
2) A GENI testbed provides a scalable live video streaming service between university campuses. It demonstrated stable 1080p video transmission with minimal bandwidth usage.
3) An SDN solution for multi-party video conferencing that constructs multicast trees. It achieved higher average video rates and lower delays compared to traditional MCU-based approaches.
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.
Web player
Hotstar is a video streaming platform which has put India on the map of tech companies. We have a diverse user base which accesses our web app from a host of browser & device combination. When it came to building our HTML5 player we chose to use open-source technologies of videojs, hls.js, and dashjs to build a secure, robust and highly performant video playe
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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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Policy-Driven Dynamic HTTP Adaptive Streaming Player Environment
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More from Minh Nguyen
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 - 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.
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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CADLAD: Device-aware Bitrate Ladder Construction for HTTP Adaptive Streaming
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WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices
- 1. WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices IEEE MMSP 2021 06.-08.10.2021 | Tampere, Finland Minh Nguyen, Ekrem Cetinkaya, Hermann Hellwagner, Christian Timmerer Christian Doppler laboratory ATHENA | Alpen-Adria University | Austria minh.nguyen@aau.at | https://athena.itec.aau.at/ 1
- 2. ● Observations ● Proposed Method ● Evaluation and Discussion ● Conclusions Agenda 2
- 4. Observations 4 Video segmentation Video encoding ... ... ... Server Client Adaptive bitrate algorithm Throughput estimation Playout buffer Video decoding ... HTTP GET requests Version 3 Version 2 Version 1 Throughput Time
- 5. Observations 5 More transferred data (higher data cost) More download time (higher buffer cost) Higher quality (less quality cost) Download high-bitrate segment
- 6. Observations 6 3 2 1 Data cost Buffer cost Quality cost Total cost Quality version Selection
- 8. WISH: User-centric Bitrate Adaptation 8 ● Throughput (data) cost of bitrate is a linearly increasing function ● Buffer cost increases when the download time increases and/or the buffer level decreases.
- 9. WISH: User-centric Bitrate Adaptation 9 ● Quality cost comprises two sub-penalties ○ Distortion penalty: When a representation is lower than the highest- bitrate representation ○ Instability penalty: When that representation is different from the average quality of recent segments Distortion penalty Instability penalty
- 10. WISH: User-centric Bitrate Adaptation 10 ● The overall cost of each representation is the weighted sum of Throughput cost, Buffer cost, and Quality cost ● Selected representation has the lowest overall cost
- 11. Weights Determination 11 ● Consider C(i) as the function of bitrate ● Weights are determined by making the maximum bitrate own the lowest cost (i.e., the derivative of ) at particular conditions:
- 12. Weights Determination 12 ● Without loss of generality ● For relaxation, we set Throughput cost = Buffer cost at the max bitrate ● Finally, the weights are defined as
- 14. Evaluation and Discussion 14 ● Experimental setup ○ HAS testbed ○ 5-min test sequences with different SI and TI ○ Bitrate ladder: {107, 240, 346, 715, 1347, 2426, 4121} kbps ○ Codec: H265/HEVC Apache Server (Ubuntu) Mobile Phone (ExoPlayer) 4G network (tc) HAS testbed Test sequences
- 15. Evaluation and Discussion 15 ● Results: Comparison with state-of-the-art approaches ○ WISH achieves the highest QoE scores for all test sequences ○ WISH’s QoE scores: from 3.46 (GamePlay) to 3.71 (ToS2) ○ Compared methods: < 3.40 ⇒ QoE score: +17.6% ITU-T QoE Score
- 16. Evaluation and Discussion 16 ● Results: Comparison with state-of-the-art approaches ○ WISH has the fewest number of stalls with at most one stall ○ WISH: < 0.5 stalls with < 1.2s length ○ BBA-0 and SQUAD: > 2 stalls with average duration 15s to 30s Number of stalls and stall duration
- 17. Evaluation and Discussion 17 ● Results: Comparison with state-of-the-art approaches ○ WISH downloads the least bitrate ○ WISH: 2053 kbps ⇒ save 7.1% to 36.4% data usage Average bitrate
- 18. Evaluation and Discussion 18 ● Results: Comparison with state-of-the-art approaches ○ WISH and ExoPlayer: high video instability and # of switches ○ BBA-0 and SQUAD: the fewest # of switches and small instability Video Instability
- 19. Evaluation and Discussion 19 ● Results: WISH’s performance with different settings ○ Service providers meet their needs of data usage by varying the safe threshold ξ ○ Higher ξ ⇒ smaller γ ⇒ less priority to high bitrate ○ Higher ξ ⇒ lower bitrate, less video instability, fewer switches and stalls WISH’s performance with different ξ values
- 20. 20 Conclusions
- 21. Conclusions 21 ● WISH: a weighted sum model to provide high QoE for mobile devices and to meet end users’ requirements ● Taking into account throughput cost, buffer cost, and quality cost of each quality level ● A mathematical solution to choose those weights ● WISH needs the lowest data usage while keeping the highest QoE scores ● In the future, integrate the retransmission technique H2BR [1] to improve the video stability of WISH [1] 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 (pp. 1-7).
- 22. Thank you 22