153 research outputs found
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Video Adaptation for High-Quality Content Delivery
Modern video players employ complex algorithms to adapt the bitrate of the video that is shown to the user. Bitrate adaptation requires a tradeoff between reducing the probability that the video freezes (rebuffers) and enhancing the quality of the video. A bitrate that is too high leads to frequent rebuffering, while a bitrate that is too low leads to poor video quality. In this dissertation we propose video-adaptation algorithms to deliver content and maximize the viewer\u27s quality of experience (QoE).
Video providers partition videos into short segments and encode each segment at multiple bitrates. The video player adaptively chooses the bitrate of each segment to download, possibly choosing different bitrates for successive segments. We formulate bitrate adaptation as a utility-maximization problem, and design algorithms to provide provably near-optimal time-average utility.
Real-world systems are generally too complex to be fully represented in a theoretical model and thus present a new set of challenges. We design algorithms that deliver video on production systems, maintaining the strengths of the theoretical algorithms while also tackling challenges faced in production. Our algorithms are now part of the official DASH reference player dash.js and are being used by video providers in production environments.
Most online video is streamed via HTTP over TCP. TCP provides reliable delivery at the expense of additional latency incurred when retransmitting lost packets and head-of-line blocking. Using QUIC allows the video player to tolerate some packet loss without incurring the performance penalties. We design and implement algorithms that exploit this added flexibility to provide higher overall QoE by reducing latency and rebuffering while allowing some packet loss.
Recently virtual reality content is increasing in popularity, and delivering 360° video comes with new challenges and opportunities. The viewing space is often partitioned in tiles, and a viewer using a head-mounted display only sees a subset of the tiles at any time. We develop an open source simulation environment for fast and reproducible testing of 360° algorithms. We develop adaptation algorithms that provide high QoE by allocating more bandwidth resources to deliver the tiles that the viewer is more likely to see, while ensuring that the video player reacts in a timely manner when the viewer changes their head pose
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QOE-AWARE CONTENT DISTRIBUTION SYSTEMS FOR ADAPTIVE BITRATE VIDEO STREAMING
A prodigious increase in video streaming content along with a simultaneous rise in end system capabilities has led to the proliferation of adaptive bit rate video streaming users in the Internet. Today, video streaming services range from Video-on-Demand services like traditional IP TV to more recent technologies such as immersive 3D experiences for live sports events. In order to meet the demands of these services, the multimedia and networking research community continues to strive toward efficiently delivering high quality content across the Internet while also trying to minimize content storage and delivery costs.
The introduction of flexible and adaptable technologies such as compute and storage clouds, Network Function Virtualization and Software Defined Networking continue to fuel content provider revenue. Today, content providers such as Google and Facebook build their own Software-Defined WANs to efficiently serve millions of users worldwide, while NetFlix partners with ISPs such as ATT (using OpenConnect) and cloud providers such as Amazon EC2 to serve their content and manage the delivery of several petabytes of high-quality video content for millions of subscribers at a global scale, respectively. In recent years, the unprecedented growth of video traffic in the Internet has seen several innovative systems such as Software Defined Networks and Information Centric Networks as well as inventive protocols such as QUIC, in an effort to keep up with the effects of this remarkable growth. While most existing systems continue to sub-optimally satisfy user requirements, future video streaming systems will require optimal management of storage and bandwidth resources that are several orders of magnitude larger than what is implemented today. Moreover, Quality-of-Experience metrics are becoming increasingly fine-grained in order to accurately quantify diverse content and consumer needs.
In this dissertation, we design and investigate innovative adaptive bit rate video streaming systems and analyze the implications of recent technologies on traditional streaming approaches using real-world experimentation methods. We provide useful insights for current and future content distribution network administrators to tackle Quality-of-Experience dilemmas and serve high quality video content to several users at a global scale. In order to show how Quality-of-Experience can benefit from core network architectural modifications, we design and evaluate prototypes for video streaming in Information Centric Networks and Software-Defined Networks. We also present a real-world, in-depth analysis of adaptive bitrate video streaming over protocols such as QUIC and MPQUIC to show how end-to-end protocol innovation can contribute to substantial Quality-of-Experience benefits for adaptive bit rate video streaming systems. We investigate a cross-layer approach based on QUIC and observe that application layer-based information can be successfully used to determine transport layer parameters for ABR streaming applications
세그먼트 교체 기법을 활용한 심층 강화학습 기반의 ABR 알고리즘
학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2021. 2. 김종권.적응형 비트레이트 알고리즘은 온라인 비디오 서비스의 재생 품질, 즉 사용자 체감 품질을 올리기 위하여 사용되는 대표적 기술 중 하나이다. 지금까지 적응형 비트레이트 알고리즘은 다양한 최적화 기법에 기반하여 사용자 체감 품질을 최적화하였다. 그러나 대부분의 적응형 비트레이트 알고리즘은 공통된 한계점을 지닌다. 사용자 체감 품질을 최적화하기 위해 단순히 다음으로 다운로드 해야하는 세그먼트의 비트레이트만을 결정한다는 점이 그 한계점으로, 이러한 유형에 속하는 적응형 비트레이트 알고리즘들은 변화하는 네트워크 환경에 맞춰 앞으로 다운로드할 세그먼트의 비트레이트는 최적으로 조정할 수 있지만 이미 다운로드한 세그먼트에 대해선 어떠한 최적화도 진행할 수 없다. 그렇기에 사용자의 네트워크 환경이 극단적으로 개선되더라도 이에 대한 활용도가 떨어진다.
이러한 한계점을 극복하기 위해 우리는 LAWS 기법, 학습 기반의 세그먼트 교체 전략을 포함한 적응형 비트레이트 알고리즘, 을 제안한다. 제안 모델은 사용자의 네트워크 환경 등에 따라서 더 나은 비트레이트로 세그먼트를 교체할 수 있다. 제안 기법을 실현하기 위해 우리는 새로운 형태의 리워드를 디자인한다. 이를 통해 제안 기법은 세그먼트 교체 전략을 포함한 형태로 사용자 체감 품질을 최적화할 수 있다. 또한 세그먼트 교체 전략을 포함함에 따라 증가하는 문제의 복잡도에 대응하기 위해 규칙 기반 행동 제약 기법을 사용하여 모델의 학습을 원하는 방향으로 유도한다. 우리는 최종적으로 심층 강화학습 기반의 적응형 비트레이트 알고리즘을 제안한다. 네트워크 트레이스를 기반으로 실시한 실험에서는 제안 기법이 기존의 기법들에 비해 사용자 체감 품질을 13.1%까지 개선시키는 것으로 확인됐다Adaptive bitrate (ABR) algorithm is one of the representative techniques used to optimize the playback quality of online video services, namely Quality of Experience (QoE). So far, ABR algorithms based on various optimization techniques have optimized QoE. However, most of the ABR algorithms proposed to date have common limitations; the range of options for optimization. Currently, most ABR algorithms only determine the bit rate of the next segment for QoE optimization. This type of ABR algorithm can optimize the bit rate of a segment to be downloaded in the future in a dynamic network environment. However, it is not possible to optimize any segment previously downloaded, so the changed network environment cannot be utilized to the maximum.
To overcome this limitation, we propose LAWS, learning based ABR algorithm with segment replacement. LAWS can be replaced with a better bit rate, even for previously downloaded segments, in conditions such as an improved network environment. First for this, we design a novel form of reward for optimization, including segment replacement. Through this, QoE, the optimization objective of the ABR algorithm, can be optimized in the form of segment replacement. In addition, we propose a rule-based learning method to solve the challenges arising in the model learning process. We finally propose an ABR algorithm with segment replacement based on deep reinforcement learning. Experiments based on network traces show that the newly proposed technique has a QoE improvement of 13.1% compared to the existing ABR techniques.I. Introduction 1
II. Related Work 4
2.1 DASH 4
2.2 Adaptive BitRate Algorithm 6
III. Motivation and Approach 9
3.1 Motivation 9
3.2 Approach 11
IV. Neural ABR algorithm with Segment Replacement 13
4.1 Action 15
4.2 State 15
4.3 Reward 18
4.4 Rule based learning 26
4.5 Implementation 27
V. Experiments 28
5.1 Experiment Setup 28
5.2 Baselines 29
5.3 Comparison with Existing ABR algorithms 33
5.4 Analyze Replacement Characteristics 35
5.5 Comparison Between Learning Based Algorithms 35
VI. Conclusion 37Maste
{VOXEL}: {C}ross-Layer Optimization for Video Streaming with Imperfect Transmission
Delivering videos under less-than-ideal network conditions without compromising end-users' quality of experiences is a hard problem. Virtually all prior work follow a piecemeal approach - -either "tweaking"the fully reliable transport layer or making the client "smarter."We propose VOXEL, a cross-layer optimization system for video streaming. We use VOXEL to demonstrate how to combine application-provided "insights"with a partially reliable protocol for optimizing video streaming. To this end, we present a novel ABR algorithm that explicitly trades off losses for improving end-users' video-watching experiences. VOXEL is fully compatible with DASH, and backward-compatible with VOXEL-unaware servers and clients. In our experiments emulating a wide range of network conditions, VOXEL outperforms the state-of-the-art: We stream videos in the 90th-percentile with up to 97% less rebuffering than the state-of-the-art without sacrificing visual fidelity. We also demonstrate the benefits of VOXEL for small-buffer regimes like the emerging use case of low-latency and live streaming. In a survey of 54 real users, 84% of the participants indicated that they prefer videos streamed using VOXEL compared to the state-of-the-art
Towards enabling cross-layer information sharing to improve today's content delivery systems
Content is omnipresent and without content the Internet would not be what it is today. End users consume content throughout the day, from checking the latest news on Twitter in the morning, to streaming music in the background (while working), to streaming movies or playing online games in the evening, and to using apps (e.g., sleep trackers) even while we sleep in the night. All of these different kinds of content have very specific and different requirements on a transport—on one end, online gaming often requires a low latency connection but needs little throughput, and, on the other, streaming a video requires high throughput, but it performs quite poorly under packet loss. Yet, all content is transferred opaquely over the same transport, adhering to a strict separation of network layers. Even a modern transport protocol such as Multi-Path TCP, which is capable of utilizing multiple paths, cannot take the (above) requirements or needs of that content into account for its path selection. In this work we challenge the layer separation and show that sharing information across the layers is beneficial for consuming web and video content. To this end, we created an event-based simulator for evaluating how applications can make informed decisions about which interfaces to use delivering different content based on a set of pre-defined policies that encode the (performance) requirements or needs of that content. Our policies achieve speedups of a factor of two in 20% of our cases, have benefits in more than 50%, and create no overhead in any of the cases. For video content we created a full streaming system that allows an even finer grained information sharing between the transport and the application. Our streaming system, called VOXEL, enables applications to select dynamically and on a frame granularity which video data to transfer based on the current network conditions. VOXEL drastically reduces video stalls in the 90th-percentile by up to 97% while not sacrificing the stream's visual fidelity. We confirmed our performance improvements in a real-user study where 84% of the participants clearly preferred watching videos streamed with VOXEL over the state-of-the-art.Inhalte sind allgegenwärtig und ohne Inhalte wäre das Internet nicht das, was es heute ist. Endbenutzer konsumieren Inhalte von früh bis spät - es beginnt am Morgen mit dem Lesen der neusten Nachrichten auf Twitter, dem online hören von Musik während der Arbeit, wird fortgeführt mit dem Schauen von Filmen über Online-Streaming Dienste oder dem spielen von Mehrspieler Online Spielen am Abend, und sogar dem, mit dem Internet synchronisierten, Überwachens des eigenen Schlafes in der Nacht. All diese verschiedenen Arten von Inhalten haben sehr spezifische und unterschiedliche Ansprüche an den Transport über das Internet - auf der einen Seite sind es Online Spiele, die eine sehr geringe Latenz, aber kaum Durchsatz benötigen, auf der Anderen gibt es Video-Streaming Dienste, die einen sehr hohen Datendurchsatz benötigen, aber, sehr nur schlecht mit Paketverlust umgehen können. Jedoch werden all diese Inhalte über den selben, undurchsichtigen, Transportweg übertragen, weil an eine strikte Unterteilung der Netzwerk- und Transportschicht festgehalten wird. Sogar ein modernes Übertragungsprotokoll wie MPTCP, welches es ermöglicht mehrere Netzwerkpfade zu nutzen, kann die (oben genannten) Anforderungen oder Bedürfnisse des Inhaltes, nicht für die Pfadselektierung, in Betracht ziehen. In dieser Arbeit fordern wir die Trennung der Schichten heraus und zeigen, dass ein Informationsaustausch zwischen den Netzwerkschichten von großem Vorteil für das Konsumieren von Webseiten und Video Inhalten sein kann. Hierzu haben wir einen Ereignisorientierten Simulator entwickelt, mit dem wir untersuchten wie Applikationen eine informierte Entscheidung darüber treffen können, welche Netzwerkschnittstellen für verschiedene Inhalte, basierend auf vordefinierten Regeln, welche die Leistungsvorgaben oder Bedürfnisse eines Inhalts kodieren, benutzt werden sollen. Unsere Regeln erreichen eine Verbesserung um einen Faktor von Zwei in 20% unserer Testfälle, haben einen Vorteil in mehr als 50% der Fälle und erzeugen in keinem Fall einen Mehraufwand. Für Video Inhalte haben wir ein komplettes Video-Streaming System entwickelt, welches einen noch feingranulareren Informationsaustausch zwischen der Applikation und des Transportes ermöglicht. Unser, VOXEL genanntes, System ermöglicht es Applikationen dynamisch und auf Videobild Granularität zu bestimmen welche Videodaten, entsprechend der aktuellen Netzwerksituation, übertragen werden sollen. VOXEL kann das stehenbleiben von Videos im 90%-Perzentil drastisch, um bis zu 97%, reduzieren, ohne dabei die visuelle Qualität des übertragenen Videos zu beeinträchtigen. Wir haben unsere Leistungsverbesserung in einer Studie mit echten Benutzern bestätigt, bei der 84% der Befragten es, im vergleich zum aktuellen Stand der Technik, klar bevorzugten Videos zu schauen, die über VOXEL übertragen wurden
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