153 research outputs found

    세그먼트 교체 기법을 활용한 심층 강화학습 기반의 ABR 알고리즘

    Get PDF
    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

    Get PDF
    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

    Get PDF
    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
    corecore