QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming

Petrangeli, Stefano; Famaey, Jeroen; Claeys, Maxim; Latré, Steven; Turck, Filip De

doi:10.1145/2818361

Cited by 99 publications

(96 citation statements)

References 28 publications

(34 reference statements)

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“…that our multi-client HAS framework resulted in a better video quality and in a remarkable improvement of fairness, up to 60% and 48% in the 10 clients case, compared to MSS and the Q-Learning-based client, respectively. In Figure 2b, a comparison in terms of QoE and freeze time is provided with the MSS, the QoE-RAHAS heuristic [9] and our multi-client solution [10], in a scenario with 30 clients streaming video at the same time. By evaluating our solution under varying network conditions and in several multi-client scenarios, we showed how the proposed approach can reduce freezes up to 75% when compared to benchmark heuristics.…”

Section: Proposed Approach and Methodologymentioning

confidence: 99%

QoE-Centric Management of Advanced Multimedia Services

Petrangeli

Turck

2015

Intelligent Mechanisms for Network Configuration and Security

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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.

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Section: Proposed Approach and Methodologymentioning

confidence: 99%

QoE-Centric Management of Advanced Multimedia Services

Petrangeli

Turck

2015

Intelligent Mechanisms for Network Configuration and Security

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“…As an example we consider QoE fairness in the context of bottleneck link sharing among adaptive video streams, where the on/off nature of flows results in inaccurate clientside bandwidth estimation and leads to a potential unfair resource demand [13,27,37].…”

Section: Fairness From the User's Perspectivementioning

confidence: 99%

“…Their metric considers a set of QoE values corresponding to bitrate allocation, calculated taking into account factors such as user screen size, resolution, and viewing distance. Further, Petrangeli et al [37] incorporate the notion of maximizing fairness, expressed as the standard deviation of clients' QoE, into a novel rate adaptation algorithm for adaptive streaming. Villa and Heegaard [45] specify a 'perceived fairness metric' as the difference between the worst and best performing streaming sessions in terms of average number of rate reductions (i.e., discrimination events) per minute.…”

Section: Fairness From the User's Perspectivementioning

confidence: 99%

“…The literature further advocates the need to consider ensuring fairness among users, in particular related to QoE fairness [13,27,37]. This generally leads to a multiobjective optimization problem, typically, maximizing QoE subject to some fairness constraints.…”

Section: Qoe Fairness and Qoe Managementmentioning

confidence: 99%

“…However, when multiple HAS clients are competing for shared network resources, they may negatively influence each other in terms of QoE [37]. Thus, a QoE fairness issue may arise due to the HAS adaptation algorithm.…”

Section: Case Study: Http Adaptive Streaming Qoementioning

confidence: 99%

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A new QoE fairness index for QoE management

et al. 2018

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The user-centric management of networks and services focuses on the Quality of Experience (QoE) as perceived by the end user. In general, the goal is to maximize (or at least ensure an acceptable) QoE, while ensuring fairness among users, e.g., in terms of resource allocation and scheduling in shared systems. A problem arising in this context is that the notions of fairness commonly applied in the QoS domain do not translate well to the QoE domain. We have recently proposed a QoE fairness index F, which solves these issues. In this paper, we provide a detailed rationale for it, along with a thorough comparison of the proposed index and its properties against the most widely used QoS fairness indices, showing its advantages. We furthermore explore the potential uses of the index, in the context of QoE management and describe future research lines on this topic.

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Software‐defined network‐based prioritization to avoid video freezes in HTTP adaptive streaming

et al. 2016

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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. It has been shown that state-of-the-art heuristics perform suboptimal when sudden bandwidth drops occur, therefore leading to freezes in the video playout, the main factor influencing users' quality of experience (QoE). This issue is aggravated in case of live events, where the client-side buffer has to be kept as small as possible in order to reduce the playout delay between the user and the live signal. In this article, we propose a framework capable of increasing the QoE of HAS clients by reducing video freezes. The framework is based on OpenFlow, a widely adopted protocol to implement the softwaredefined networking principle. An OpenFlow controller is in charge of introducing prioritized delivery of HAS segments, based on the network conditions and the HAS clients' status. Particularly, the HAS clients' status is obtained without any explicit clients-to-controller communication, and thus, no extra signaling is introduced into the network. Moreover, this OpenFlow controller is transparent to the quality decision process of the clients, as it assists the delivery of the segments, but it does not determine the actual quality to be requested. In order to provide a comprehensive analysis of the proposed approach, we investigate the performance of the proposed OpenFlow-based framework in the presence of realistic Internet cross-traffic. Particularly, we model two types of applications, namely, HTTP web browsing and progressive download video streaming, which currently represent the majority of Internet traffic together with HAS. By evaluating this novel approach through emulation in several multi-client scenarios, we show how the proposed approach can reduce freeze time for the HAS clients due to network congestion up to 10 times compared with stateof-the-art heuristics, without impacting the performance of the cross-traffic applications. SOFTWARE-DEFINED NETWORK-BASED PRIORITIZATION TO AVOID VIDEO FREEZES IN HAS 249 the available resources. Such dynamic adaptation results in a smooth video streaming experience. Nevertheless, several inefficiencies still have to be solved in order to further improve users' quality of experience (QoE). As reported by Akshabi et al. and Riiser et al., current rate adaptation heuristics perform quality selection sub-optimally, especially when a sudden bandwidth drop occurs [1,2]. This leads to unnecessary quality switches and video playout interruptions, which negatively affect the final QoE of the users. Similar conclusions are drawn in the 2015 Conviva report on HAS [3]. The report reveals that almost 29% of the analyzed HAS sessions exhibit at least one video freeze. This problem is mainly due to the unmanaged nature of the current HAS technologi...

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QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming

Cited by 99 publications

References 28 publications

QoE-Centric Management of Advanced Multimedia Services

QoE-Centric Management of Advanced Multimedia Services

A new QoE fairness index for QoE management

Software‐defined network‐based prioritization to avoid video freezes in HTTP adaptive streaming

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