Quality impact of scalable video coding tunneling for media aware content delivery
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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.
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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.Scalable Video Coding Guidelines and Performance Evaluations for Adaptive Me...
Presentation of the paper at the 18th IEEE Symposium on Computers and Communication (ISCC 2013), Split, Croatia, July 2013.
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.
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×.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)
Bitmovin AV1/VVC Presentation_Streaming Media East by Christian Feldmann
This document provides an overview and update on AV1 and VVC video coding standards. It summarizes the novel technical features of each, including AV1 improvements like overlapped block motion compensation and affine motion, and VVC features like triangle partitioning and decoder-side motion refinement. Performance results show VVC provides around 30% better compression than HM 16.20, while AV1 is around 20% better. Both standards are still in development and neither has wide adoption yet. VVC licensing is unknown while AV1 remains free and open.
ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Seq...
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HTTP Adaptive Streaming of video content is becoming an integral part of the Internet and accounts for the majority of today’s traffic. Although Internet bandwidth is constantly increasing, video compression technology plays an important role and the major challenge is to select and set up multiple video codecs, each with hundreds of transcoding parameters. Additionally, the transcoding speed depends directly on the selected transcoding parameters and the infrastructure used. Predicting transcoding time for multiple transcoding parameters with different codecs and processing units is a challenging task, as it depends on many factors. This paper provides a novel and considerably fast method for transcoding time prediction using video content classification and neural network prediction. Our artificial neural network (ANN) model predicts the transcoding times of video segments for state-of-the-art video codecs based on transcoding parameters and content complexity. We evaluated our method for two video codecs/implementations (AVC/x264 and HEVC/x265) as part of large-scale HTTP Adaptive Streaming services. The ANN model of our method is able to predict the transcoding time by minimizing the mean absolute error (MAE) to 1.37 and 2.67 for x264 and x265 codecs, respectively. For x264, this is an improvement of 22% compared to the state of the art.Quality of Experience of Web-based Adaptive HTTP Streaming Clients in Real-Wo...
Quality of Experience of Web-based Adaptive HTTP Streaming Clients in Real-Wo...Alpen-Adria-Universität
This document summarizes a presentation on evaluating the quality of experience (QoE) of adaptive HTTP streaming clients like DASH in real-world environments using crowdsourcing. The study evaluated three clients (DASH-JS, dash.js, YouTube) playing video content with different quality representations. Results found that DASH-JS had high startup times but low stalls and good throughput/QoE, while dash.js had low startup but high stalls and lower throughput/QoE. YouTube performed best with low startup, few stalls, and highest throughput and QoE. The methodology and results provide insights into QoE aspects of DASH clients over the open internet.Is One Second Enough? Evaluating QoE for Inter-Destination Multimedia Synchro...
Is One Second Enough? Evaluating QoE for Inter-Destination Multimedia Synchro...Alpen-Adria-Universität
Modern-age technology enables us to consume multimedia for enjoyment and as a social experience. The traditional way to consume multimedia together (e.g., with family or friends in the living room) is being superseded by a location-independent scenario where geographically distributed users consume the same content while having a real-time communication channel among each other. Inter-Destination Multimedia Synchronization (IDMS) is the tool of choice in order to enable users a high-quality multimedia experience. In this paper, we investigate the influence of asynchronism when consuming multimedia content together while being geographically distributed. In particular, we adopt the concept of human computation and developed a reaction game which we used to conduct a crowdsourced subjective quality assessment in order to evaluate a threshold for multimedia synchronization within an IDMS scenario. Our results show a significant decrease in overall Quality of Experience (QOE) at an asynchronism level of 750ms. At the same time, we were able to show that asynchronism at a level of 400ms does not have significant differences regarding the QoE when compared to the synchronous reference case.More Related Content
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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.WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile D...
Recently, mobile devices have become paramount in online video streaming. Adaptive bitrate (ABR) algorithms of players responsible for selecting the quality of the videos face critical challenges in providing a high Quality of Experience (QoE) for end users. One open issue is how to ensure the optimal experience for heterogeneous devices in the context of extreme variation of mobile broadband networks. Additionally, end users may have different priorities on video quality and data usage (i.e., the amount of data downloaded to the devices through the mobile networks). A generic mechanism for players that enables specification of various policies to meet end users’ needs is still missing. In this paper, we propose a weighted sum model, namely WISH, that yields high QoE of the video and allows end users to express their preferences among different parameters (i.e., data usage, stall events, and video quality) of video streaming. WISH has been implemented into ExoPlayer, a popular player used in many mobile applications. The experimental results show that WISH improves the QoE by up to 17.6% while saving 36.4% of data usage compared to state-of-the-art ABR algorithms and provides dynamic adaptation to end users’ requirements.
Docker-Based Evaluation Framework for Video Streaming QoE in Broadband Networks
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Video streaming is one of the top traffic contributors in the Internet and a frequent research subject. It is expected that streaming traffic will grow 4-fold for video globally and 9-fold for mobile video between 2017 and 2022. In this paper, we present an automatized measurement framework for evaluating video streaming QoE in operational broadband networks, using headless streaming with a Docker-based client, and a server-side implementation allowing for the use of multiple video players and adaptation algorithms. Our framework allows for integration with the MONROE testbed and Bitmovin Analytics, which bring on the possibility to conduct large-scale measurements in different networks, including mobility scenarios, and monitor different parameters in the application, transport, network, and physical layers in real-time.Where to Encode: A Performance Analysis of Intel x86 and Arm-based Amazon EC2...
Where to Encode: A Performance Analysis of Intel x86 and Arm-based Amazon EC2...Alpen-Adria-Universität
Video streaming became an undivided part of the Internet. To efficiently utilise the limited network bandwidth it is essential to encode the video content. However, encoding is a computationally intensive task, involving high-performance resources provided by private infrastructures or public clouds. Public clouds, such as Amazon EC2, provide a large portfolio of services and instances optimized for specific purposes and budgets. The majority of Amazon’s instances use x86 processors, such as Intel Xeon or AMD EPYC. However, following the recent trends in computer architecture, Amazon introduced Arm based instances that promise up to 40% better cost performance
ratio than comparable x86 instances for specific workloads. We evaluate in this paper the video encoding performance of x86 and Arm instances of four instance families using the latest FFmpeg version and two video codecs. We examine the impact of the encoding parameters, such as different presets and bitrates, on the time and cost for encoding. Our experiments reveal that Arm instances show high time and cost saving potential of up to
33.63% for specific bitrates and presets, especially for the x264 codec. However, the x86 instances are more general and achieve low encoding times, regardless of the codec.Machine Learning Based Video Coding Enhancements for HTTP Adaptive Streaming
This document discusses machine learning based enhancements for video coding and HTTP adaptive streaming. It introduces the research questions around efficiently providing multi-rate video representations over different resolutions for adaptive streaming, improving video codec performance with machine learning, improving video quality with machine learning, and using machine learning for perceptual quality assessment. It outlines the methodology, design process, and existing results from papers on fast multi-rate encoding using information from reference representations and machine learning models. Ongoing and future work is focused on super-resolution, perceptual quality assessment with machine learning, and improving in-loop filtering with machine learning.
LwTE: Light-weight Transcoding at the Edge
Farzad Tashtarian is invited to talk on “LwTE: Light-weight Transcoding at the Edge” at IMDEA Networks Institute, Madrid, Spain.
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.
SLFC: 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.
H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...
H2BR: An HTTP/2-based Retransmission Technique to Improve the QoE of Adaptive...Alpen-Adria-Universität
HTTP-based Adaptive Streaming (HAS) plays a key role in over-the-top video streaming. It contributes towards reducing the rebuffering duration of video playout by adapting the video quality to the current network conditions. However, it incurs variations of video quality in a streaming session because of the throughput fluctuation, which impacts the user's Quality of Experience (QoE). Besides, many adaptive bitrate (ABR) algorithms choose the lowest-quality segments at the beginning of the streaming session to ramp up the playout buffer as soon as possible. Although this strategy decreases the startup time, the users can be annoyed as they have to watch a low-quality video initially. In this paper, we propose an efficient retransmission technique, namely H2BR, to replace low-quality segments being stored in the playout buffer with higher-quality versions by using features of HTTP/2 including (i) stream priority, (ii) server push, and (iii) stream termination. The experimental results show that H2BR helps users avoid watching low video quality during video playback and improves the user's QoE. H2BR can decrease by up to more than 70% the time when the users suffer the lowest-quality video as well as benefits the QoE by up to 13%.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.
MiPSO: Multi-Period Per-Scene Optimization For HTTP Adaptive Streaming
Video delivery over the Internet has become more and more established in recent years due to the widespread use of Dynamic Adaptive Streaming over HTTP (DASH). The current DASH specification defines a hierarchical data model for Media Presentation Descriptions (MPDs) in terms of periods, adaptation sets, representations, and segments. Although multi-period MPDs are widely used in live streaming scenarios, they are not fully utilized in Video-on-Demand (VoD) HTTP adaptive streaming (HAS) scenarios. In this paper, we introduce MiPSO, a framework for Multi-Period per-Scene optimization, to examine multiple periods in VoD HAS scenarios. MiPSO provides different encoded representations of a video at either (i) maximum possible quality or (ii) minimum possible bitrate, beneficial to both service providers and subscribers. In each period, the proposed framework adjusts the video representations (resolution-bitrate pairs) by taking into account the complexities of the video content, with the aim of achieving streams at either higher qualities or lower bitrates. The experimental evaluation with a test video data set shows that MiPSO reduces the average bitrate of streams with the same visual quality by approximately 10% or increases the visual quality of streams by at least 1 dB in terms of Peak Signal-to-Noise (PSNR) at the same bitrate compared to conventional approaches.
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.
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.aspFaME-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.Scalable Video Coding Guidelines and Performance Evaluations for Adaptive Me...
Presentation of the paper at the 18th IEEE Symposium on Computers and Communication (ISCC 2013), Split, Croatia, July 2013.
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.
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×.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)
Bitmovin AV1/VVC Presentation_Streaming Media East by Christian Feldmann
This document provides an overview and update on AV1 and VVC video coding standards. It summarizes the novel technical features of each, including AV1 improvements like overlapped block motion compensation and affine motion, and VVC features like triangle partitioning and decoder-side motion refinement. Performance results show VVC provides around 30% better compression than HM 16.20, while AV1 is around 20% better. Both standards are still in development and neither has wide adoption yet. VVC licensing is unknown while AV1 remains free and open.
ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Seq...
ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Seq...Alpen-Adria-Universität
HTTP Adaptive Streaming of video content is becoming an integral part of the Internet and accounts for the majority of today’s traffic. Although Internet bandwidth is constantly increasing, video compression technology plays an important role and the major challenge is to select and set up multiple video codecs, each with hundreds of transcoding parameters. Additionally, the transcoding speed depends directly on the selected transcoding parameters and the infrastructure used. Predicting transcoding time for multiple transcoding parameters with different codecs and processing units is a challenging task, as it depends on many factors. This paper provides a novel and considerably fast method for transcoding time prediction using video content classification and neural network prediction. Our artificial neural network (ANN) model predicts the transcoding times of video segments for state-of-the-art video codecs based on transcoding parameters and content complexity. We evaluated our method for two video codecs/implementations (AVC/x264 and HEVC/x265) as part of large-scale HTTP Adaptive Streaming services. The ANN model of our method is able to predict the transcoding time by minimizing the mean absolute error (MAE) to 1.37 and 2.67 for x264 and x265 codecs, respectively. For x264, this is an improvement of 22% compared to the state of the art.What's hot (20)
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Understanding Quality of Experience of Heuristic-based HTTP Adaptive Bitrate ...
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Docker-Based Evaluation Framework for Video Streaming QoE in Broadband Networks
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Where to Encode: A Performance Analysis of Intel x86 and Arm-based Amazon EC2...
Where to Encode: A Performance Analysis of Intel x86 and Arm-based Amazon EC2...
Machine Learning Based Video Coding Enhancements for HTTP Adaptive Streaming
Machine Learning Based Video Coding Enhancements for HTTP Adaptive Streaming
Bandwidth Prediction in Low-Latency Chunked Streaming
Bandwidth Prediction in Low-Latency Chunked 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...
CAdViSE or how to find the Sweet Spots of ABR Systems
CAdViSE or how to find the Sweet Spots of ABR Systems
MiPSO: Multi-Period Per-Scene Optimization For HTTP Adaptive Streaming
MiPSO: Multi-Period Per-Scene Optimization For HTTP Adaptive Streaming
On Optimizing Resource Utilization in AVC-based Real-time Video Streaming
On Optimizing Resource Utilization in AVC-based Real-time Video Streaming
Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of th...
Video Coding for Large-Scale HTTP Adaptive Streaming Deployments: State of th...
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...
FaME-ML: Fast Multirate Encoding for HTTP Adaptive Streaming Using Machine Le...
Scalable Video Coding Guidelines and Performance Evaluations for Adaptive Me...
Scalable Video Coding Guidelines and Performance Evaluations for Adaptive Me...
Labmeeting - 20150831 - Overhead and Performance of Low Latency Live Streamin...
Labmeeting - 20150831 - Overhead and Performance of Low Latency Live Streamin...
Quality Optimization of Live Streaming Services over HTTP with Reinforcement ...
Quality Optimization of Live Streaming Services over HTTP with Reinforcement ...
Bitmovin AV1/VVC Presentation_Streaming Media East by Christian Feldmann
Bitmovin AV1/VVC Presentation_Streaming Media East by Christian Feldmann
ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Seq...
ComplexCTTP: Complexity Class Based Transcoding Time Prediction for Video Seq...
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This document summarizes an overview presentation on over-the-top content delivery and HTTP adaptive streaming. It discusses example services like Netflix, HBO Go, and BBC iPlayer. It also covers media delivery over the Internet, including the differences between managed IPTV delivery and unmanaged over-the-top delivery. The presentation also provides an overview of HTTP adaptive streaming building blocks and workflows for content generation, distribution, and consumption.
Adaptive Media Streaming: The Role of Standards
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Quality of Experience in Multimedia Systems and Services: A Journey Towards t...
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In computing and communications systems, quality is often difficult to define. Attempts to understand this concept date back to Aristotle, who included quality as one of his 10 categories of human apprehension. ISO standard 8402:1986 defines quality as “the totality of features and characteristics of a product or service that bears its ability to satisfy stated or implied needs,” which embraces objective as well as subjective parameters. In practice, however, quality could be compared to the elephant in the famous Indian parable about a group of blind men who each feels a different part of the animal and, thus, they disagree as to what it looks like....MPEG-DASH: Overview, State-of-the-Art, and Future Roadmap
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This summary provides the key details about the document in 3 sentences:
The document describes the implementation of a 1.2-V 500-MHz programmable gain amplifier (PGA) for high-definition video digitizers in a 65-nm CMOS process. The PGA uses a pseudo switched-capacitor architecture that buffers the video signal without switching during active video and uses the switched-capacitor circuitry for setup of the DC operating point during blanking periods. Simulation results show the PGA maintains -3dB bandwidth of 550 MHz and distortion of -60dB for a 30-MHz 850mVpp HD video signal while consuming 10.4mW power.
Online Bitrate ladder prediction for Adaptive VVC Streaming
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Digital communication unit 1
UNIT I SAMPLING & QUANTIZATION
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MaxEye DVB Test and Measurement Solutions Overview
MaxEye Technologies provides complete DVB test and measurement solutions for testing the broadcast equpiments and receivers during manufacturing, design labs and automating the tests. The supported DVB Standards are DVB-T/T2/H, DVB-S/S2, DVB-C, ISDB-T/Tb, ATSC, ATSC-M/H, DAB/DAB Plus/T-DMB, DRM and DRM Plus. In addition to the digital standards we also support AM, FM and RDS. For more information contact ramesh@maxeyetech.com
1661_10188_090415_1.ppt
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Macroblock Level Rate Control for Low Delay H.264/AVC based Video Communication
We propose a macro-block (MB) level rate control
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Trev 300 morello
Alberto Morello and Vittoria Mignone
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Among the various network protocols that can be
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to errors; it can become critical when UDP is used. In such
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09a video compstream_intro_trd_23-nov-2005v0_2
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Scalable Media Delivery Chain with Distributed Adaptation
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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.
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The document discusses providing quality assurance for video streaming across the internet using a proxy server system. It proposes a staggered two-flow streaming approach where an unreliable flow for enhanced video data is one segment ahead of a reliable flow for essential data. This allows the reliable flow to be prefetched and cached at the proxy server to ensure quality even with bandwidth limitations in the best-effort network. Experimental results show the approach can provide stable performance with low packet losses compared to using standard TCP. Future work areas include improving scalability and implementing application-aware bandwidth management and admission control.
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2. Techniques like adaptive media playout, rate-distortion optimal packet scheduling, and network-adaptive packet dependency management can reduce latency and packet loss while maximizing reconstruction quality.
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Analog to digital converter (ACD)
An analog to digital converter (ADC) allows digital circuits to interface with the real world by converting analog signals, like sound from a microphone, into digital data. ADCs work by sampling the analog signal at discrete time intervals and then quantizing the signal amplitude into discrete levels represented by a binary code. The sampling rate must be at least twice the highest frequency component of the analog signal per the Nyquist criterion. The resolution of an ADC, defined as the smallest voltage increment it can detect, is determined by the number of bits in its output. Common applications of ADCs include data acquisition, control systems, sensors, audio/video devices, and more.
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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.
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances
Video streaming constitutes 65 % of global internet traffic, prompting an investigation into its energy consumption and CO2 emissions. Video encoding, a computationally intensive part of streaming, has moved to cloud computing for its scalability and flexibility. However, cloud data centers’ energy consumption, especially video encoding, poses environmental challenges. This paper presents VEED, a FAIR Video Encoding Energy and CO2 Emissions Dataset for Amazon Web Services (AWS) EC2 instances. Additionally, the dataset also contains the duration, CPU utilization, and cost of the encoding. To prepare this dataset, we introduce a model and conduct a benchmark to estimate the energy and CO2 emissions of different Amazon EC2 instances during the encoding of 500 video segments with various complexities and resolutions using Advanced Video Coding (AVC)
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In HTTP adaptive live streaming applications, video segments are encoded at a fixed set of bitrate-resolution pairs known as bitrate ladder. Live encoders use the fastest available encoding configuration, referred to as preset, to ensure the minimum possible latency in video encoding. However, an optimized preset and optimized number of CPU threads for each encoding instance may result in (i) increased quality and (ii) efficient CPU utilization while encoding. For low latency live encoders, the encoding speed is expected to be more than or equal to the video framerate. To this light, this paper introduces a Just Noticeable Difference (JND)-Aware Low latency Encoding Scheme (JALE), which uses random forest-based models to jointly determine the optimized encoder preset and thread count for each representation, based on video complexity features, the target encoding speed, the total number of available CPU threads, and the target encoder. Experimental results show that, on average, JALE yield a quality improvement of 1.32 dB PSNR and 5.38 VMAF points with the same bitrate, compared to the fastest preset encoding of the HTTP Live Streaming (HLS) bitrate ladder using x265 HEVC open-source encoder with eight CPU threads used for each representation. These enhancements are achieved while maintaining the desired encoding speed. Furthermore, on average, JALE results in an overall storage reduction of 72.70%, a reduction in the total number of CPU threads used by 63.83%, and a 37.87% reduction in the overall encoding time, considering a JND of six VMAF points.VEEP: Video Encoding Energy and CO₂ Emission Prediction
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Content-adaptive Video Coding for HTTP Adaptive Streaming
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Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video...
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Quality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate.
By leveraging the Microworkers crowdsourcing platform, we conduct studies with 120 subjects who provide 14,400 individual scores. According to the subjective studies, a session of about 22 minutes empowers a viewer to construct a personalized QoE model that, compared to the best of the 10 baseline models, delivers the average accuracy improvement of at least 42% for all viewers and at least 85% for the atypical viewers. The large-scale simulations based on a new technique of synthetic profiling expand the evaluation scope by exploring iQoE design choices, parameter sensitivity, and generalizability.Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Vid...
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Quality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate.
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HTTP Adaptive Streaming (HAS) methods divide a video into smaller segments, encoded at multiple pre-defined bitrates to construct a bitrate ladder. Bitrate ladders are usually optimized per title over several dimensions, such as bitrate, resolution, and framerate. This paper adds a new dimension to the bitrate ladder by considering the energy consumption of the encoding process. Video encoders often have multiple pre-defined presets to balance the trade-off between encoding time, energy consumption, and compression efficiency. Faster presets disable certain coding tools defined by the codec to reduce the encoding time at the cost of reduced compression efficiency. Firstly, this paper evaluates the energy consumption and compression efficiency of different x265 presets for 500 video sequences. Secondly, optimized presets are selected for various representations in a bitrate ladder based on the results to guarantee a minimal drop in video quality while saving energy. Finally, a new per-title model, which optimizes the trade-off between compression efficiency and energy consumption, is proposed. The experimental results show that decreasing the VMAF score by 0.15 and 0.39 while choosing an optimized preset results in encoding energy savings of 70% and 83%, respectively.Energy-Efficient Multi-Codec Bitrate-Ladder Estimation for Adaptive Video Str...
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With the emergence of multiple modern video codecs, streaming service providers are forced to encode, store, and transmit bitrate ladders of multiple codecs separately, consequently suffering from additional energy costs for encoding, storage, and transmission.
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Machine Learning Based Resource Utilization Prediction in the Computing Conti...Alpen-Adria-Universität
This paper presents UtilML, a novel approach for tackling resource utilization prediction challenges in the computing continuum. UtilML leverages Long-Short-Term Memory (LSTM) neural networks, a machine learning technique, to forecast resource utilization accurately. The effectiveness of UtilML is demonstrated through its evaluation of data extracted from a real GPU cluster in a computing continuum infrastructure comprising more than 1800 computing devices. To assess the performance of UtilML, we compared it with two related approaches that utilize a Baseline-LSTM model. Furthermore, we analyzed the LSTM results against User-Predicted values provided by GPU cluster owners for task deployment with estimated allocation values. The results indicate that UtilML outperformed user predictions by 2% to 27% for CPU utilization prediction. For memory prediction, UtilML variants excelled, showing improvements of 17% to 20% compared to user predictions.Evaluation of Quality of Experience of ABR Schemes in Gaming Stream
The exponential growth of computer game streaming has led to the development of Quality of Experience (QoE) metrics to evaluate user satisfaction and enjoyment during online gameplay and live streaming. Adaptive Bitrate (ABR) streaming is a recent technology that has been suggested to improve QoE. This method enhances the streaming experience, upholds visual quality, minimizes stall events, and boosts player retention. It achieves this by estimating network bottlenecks and selecting appropriate versions of the content that best match the available bandwidth rather than adjusting encoding parameters. To investigate the correlation between quality switching and stall events, a subjective test was conducted separately and comparatively with 71 participants. For more detailed and in-depth research, video games were analyzed with the Video Complexity Analyzer (VCA) tool and divided into three categories of different genres, camera view, and temporal complexity heatmap from the two sets of normal and action scenes. This study seeks to shed light on three unresolved issues pertinent to QoE in game streaming: (i) the user preferences towards quality switching and stall events across varied scenes and games, (ii) the user inclinations towards either a single, prolonged stall event or multiple, shorter stall events, and (iii) the impact of conspicuous quality switching on the user’s QoE. Results from the study provided valuable insights, both qualitatively and quantitatively. The study found a marked preference among users for quality switching over stall events across all types of game streaming, irrespective of the scene’s intensity. Furthermore, it was observed that multiple short-stall events were generally favored over a single long-stall event in streaming first-person shooting games. Interestingly, approximately half of the participants remained oblivious to quality switching during their game viewing sessions, and among those who noticed a change in quality, the alteration did not significantly impact their perceived QoE.
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...
Network-Assisted Delivery of Adaptive Video Streaming Services through CDN, S...Alpen-Adria-Universität
Multimedia applications, mainly video streaming services, are currently the dominant source of network load worldwide. In recent Video-on-Demand (VoD) and live video streaming services, traditional streaming delivery techniques have been replaced by adaptive solutions based on the HTTP protocol. Current trends toward high-resolution (e.g., 8K) and/or low- latency VoD and live video streaming pose new challenges to end-to-end (E2E) bandwidth demand and have stringent delay requirements. To do this, video providers typically rely on Content Delivery Networks (CDNs) to ensure that they provide scalable video streaming services. To support future streaming scenarios involving millions of users, it is necessary to increase the CDNs’ efficiency. It is widely agreed that these requirements may be satisfied by adopting emerging networking techniques to present Network-Assisted Video Streaming (NAVS) methods. Motivated by this, this thesis goes one step beyond traditional pure client- based HAS algorithms by incorporating (an) in-network component(s) with a broader view of the network to present completely transparent NAVS solutions for HAS clients.Multi-access Edge Computing for Adaptive Video Streaming
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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.
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...
VE-Match: Video Encoding Matching-based Model for Cloud and Edge Computing In...Alpen-Adria-Universität
The considerable surge in energy consumption within data centers can be attributed to the exponential rise in demand for complex computing workflows and storage resources. Video streaming applications are both compute and storage-intensive and account for the majority of today’s internet services. In this work, we designed a video encoding application consisting of codec, bitrate, and resolution set for encoding a video segment. Then, we propose VE-Match, a matching-based method to schedule video encoding applications on both Cloud and Edge resources to optimize costs and energy consumption. Evaluation results on a real computing testbed federated between Amazon Web Services (AWS) EC2 Cloud instances and the Alpen-Adria University (AAU) Edge server reveal that VE-Match achieves lower costs by 17%-78% in the cost-optimized scenarios compared to the energy-optimized and tradeoff between cost and energy. Moreover, VE-Match improves the video encoding energy consumption by 38%-45% and gCO2 emission by up to 80 % in the energy-optimized scenarios compared to the cost-optimized and tradeoff between cost and energy.Energy Consumption in Video Streaming: Components, Measurements, and Strategies
Energy Consumption in Video Streaming: Components, Measurements, and StrategiesAlpen-Adria-Universität
This document discusses energy consumption in video streaming. It identifies the key components that consume energy, including encoding, storage, networks, edge components, decoding, and displays. Measurement tools and challenges are also covered. Strategies to reduce energy usage at each component are proposed, such as efficient encoding, optimized bitrates, CDN optimization, and energy-aware networking. A holistic, end-to-end approach is needed to minimize total energy consumption in video streaming.Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...
Exploring the Energy Consumption of Video Streaming: Components, Challenges, ...Alpen-Adria-Universität
The rapid growth of video streaming usage is a significant source of energy consumption, driven by improved internet connections and service offerings, the quick development of video entertainment, the deployment of Ultra High-Definition, Virtual and Augmented Reality, as well as an increasing number of video surveillance and IoT applications. However, it is essential to note that these advancements come at the cost of energy consumption. To address this challenge, it is essential to understand the various components involved in energy consumption during video streaming, ranging from video encoding to decoding and displaying the video on the end user’s screen. Then, it is critical to accurately measure energy consumption for each component and conduct an in-depth analysis to develop energy-efficient strategies that optimize video streaming. I categorize these components into three categories: (i) data centers, (ii) networks, and (iii) end-user devices.
In this talk, my objective is to provide insights into the components of video streaming that contribute to energy consumption and highlight the challenges associated with measuring their energy usage. I will also introduce the tools that can be used for energy measurements for those components and the possible and associated strategies that lie within energy efficiency. By accurately measuring energy consumption, digital media companies can effectively monitor and control their energy usage, ultimately leading to cost savings and improved sustainability.Video Coding Enhancements for HTTP Adaptive Streaming Using Machine Learning
Video is evolving into a crucial tool as daily lives are increasingly centered around visual communication. The demand for better video content is constantly rising, from entertainment to business meetings. The delivery of video content to users is of utmost significance. HTTP adaptive streaming, in which the video content adjusts to the changing network circumstances, has become the de-facto method for delivering internet video.
As video technology continues to advance, it presents a number of challenges, one of which is the large amount of data required to describe a video accurately. To address this issue, it is necessary to have a powerful video encoding tool. Historically, these efforts have relied on hand-crafted tools and heuristics. However, with the recent advances in machine learning, there has been increasing exploration into using these techniques to enhance video coding performance.
This thesis proposes eight contributions that enhance video coding performance for HTTP adaptive streaming using machine learning.
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- 1. QUALITY IMPACT OF SCALABLE VIDEO CODING TUNNELING FOR MEDIA-AWARE CONTENT DELIVERY Michael Grafl , Christian Timmerer, Hermann Hellwagner Alpen-Adria-Universität Klagenfurt, Austria Workshop on Multimedia-Aware Networking 2011 – ICME'11 M. Grafl, C. Timmerer, H. Hellwagner Quality Impact of SVC Tunneling
- 6. SVC TUNNELING IN ALICANTE M. Grafl, C. Timmerer, H. Hellwagner Quality Impact of SVC Tunneling Source: [2]
- 8. RESULTS: MPEG-2 SVC MPEG-2 M. Grafl, C. Timmerer, H. Hellwagner Quality Impact of SVC Tunneling Software: ffmpeg (SVN-r25599) GPL MPEG-1/2 DirectShow Decoder Filter (v0.1.2) MainConcept SVC/AVC/H.264 Video Encoder & Decoder (v1.0.0.236699)
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