A multi-agent Q-Learning-based framework for achieving fairness in HTTP Adaptive Streaming

Intelligent Mechanisms for Network Configuration and Security

Turck

2015

Self Cite

Abstract. Over the last years, multimedia content has become more prominent than ever. Particularly, video streaming is responsible for more than a half of the total global bandwidth consumption on the Internet. As the original Internet was not designed to deliver such real-time, bandwidth-consuming applications, a serious challenge is posed on how to efficiently provide the best service to the users. This requires a shift in the classical approach used to deliver multimedia content, from a pure Quality of Service (QoS) to a full Quality of Experience (QoE) perspective. While QoS parameters are mainly related to low-level network aspects, the QoE reflects how the end-users perceive a particular multimedia service. As the relationship between QoS parameters and QoE is far from linear, a classical QoS-centric delivery is not able to fully optimize the quality as perceived by the users. This paper provides an overview of the main challenges this PhD aims to tackle in the field of end-to-end QoE optimization of video streaming services and, more precisely, of HTTP Adaptive Streaming (HAS) solutions, which are quickly becoming the de facto standard for video delivery over the Internet.

Section: Proposed Approach and Methodologymentioning

confidence: 99%

Section: Proposed Approach and Methodologymentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

See 1 more Smart Citation

QoE-Centric Management of Advanced Multimedia Services

Intelligent Mechanisms for Network Configuration and Security

Turck

2015

Self Cite

“…Petrangeli et al suggest an approach in which each client learns to select the most appropriate quality level, maximizing a reward based both on its own QoE and on the QoE perceived by other clients [12]. To this end, a coordination proxy estimates all perceived rewards and generates a global signal that is sent periodically to all clients.…”

Section: Related Work a Http Adaptive Streamingmentioning

confidence: 99%

An HTTP/2 push-based approach for SVC adaptive streaming

Hooft

NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium

Bouten

et al. 2016

Self Cite

Abstract-HTTP Adaptive Streaming (HAS) is the de facto standard for over-the-top video streaming. In HAS, video content is encoded at multiple quality levels and temporally divided into multiple segments. The client can select the quality level for every video segment, allowing smoother playback and a better Quality of Experience (QoE). Although results are promising, current solutions often suffer from high round-trip time (RTT) cycles in mobile networks. This is especially true for scalable video coding (SVC), where multiple requests are required to retrieve a single video segment. Meanwhile, the IETF has standardized the HTTP/2 protocol since February 2015, providing new features that allow a reduction of the page load time in web browsing. In this paper, we propose a novel approach based on HTTP/2's server push feature to actively push the base layer of live, SVCencoded content from server to client. This allows to eliminate one RTT cycle for every video segment, which has a significant impact on the user's QoE. Evaluating the proposed approach, we show that compared with HTTP/1.1, an improvement of 65.42% can be achieved for the average video quality in high-RTT networks. Compared to an AVC-based solution, the freeze frequency and duration are reduced by 54.55% and 53.06% respectively, while the loss in video quality is limited to 4.51%. Since playout freezes should be avoided at the cost of a lower video quality, we conclude that the proposed approach beneficially impacts the user's QoE.

“…This value is subsequently used by a QoE proxy in charge of intercepting and rewriting clients' requests to match the requested quality level with the optimal rate. In our previous work [14], an intermediate node collects QoE statistics on the clients' behaviour and returns this information to them. This measurement is used by the clients to obtain fairness from a QoE point of view.…”

mentioning

confidence: 99%

Network-based dynamic prioritization of HTTP adaptive streams to avoid video freezes

2015 IFIP/IEEE International Symposium on Integrated Network Management (IM)

Wauters

Huysegems³

et al. 2015

Self Cite

Abstract-HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for video streaming services over the Internet. In HAS, each video is segmented and stored in different qualities. Rate adaptation heuristics, deployed at the client, allow the most appropriate quality level to be dynamically requested, based on the current network conditions. Current heuristics under-perform when sudden bandwidth drops occur, therefore leading to freezes in the video play-out, the main factor influencing users' Quality of Experience (QoE). In this article, we propose an Openflowbased framework capable of increasing clients' QoE by reducing video freezes. An Openflow-controller is in charge of introducing prioritized delivery of HAS segments, based on feedback collected from both the network nodes and the clients. To reduce the sideeffects introduced by prioritization on the bandwidth estimation of the clients, we introduce a novel mechanism to inform the clients about the prioritization status of the downloaded segments without introducing overhead into the network. This information is then used to correct the estimated bandwidth in case of prioritized delivery. By evaluating this novel approach through emulation, under varying network conditions and in several multi-client scenarios, we show how the proposed approach can reduce freezes up to 75% compared to state-of-the-art heuristics.