QoE model for video delivered over an LTE network using HTTP adaptive streaming

HTTP Adaptive Streaming (HAS) is quickly becoming the de facto standard for video streaming services. In HAS, each video is temporally segmented and stored in different quality levels. Rate adaptation heuristics, deployed at the video player, allow the most appropriate level to be dynamically requested, based on the current network conditions. It has been shown that today's heuristics underperform when multiple clients consume video at the same time, due to fairness issues among clients. Concretely, this means that different clients negatively influence each other as they compete for shared network resources. In this article, we propose a novel rate adaptation algorithm called FINEAS (Fair In-Network Enhanced Adaptive Streaming), capable of increasing clients' Quality of Experience (QoE) and achieving fairness in a multiclient setting. A key element of this approach is an in-network system of coordination proxies in charge of facilitating fair resource sharing among clients. The strength of this approach is threefold. First, fairness is achieved without explicit communication among clients and thus no significant overhead is introduced into the network. Second, the system of coordination proxies is transparent to the clients, that is, the clients do not need to be aware of its presence. Third, the HAS principle is maintained, as the in-network components only provide the clients with new information and suggestions, while the rate adaptation decision remains the sole responsibility of the clients themselves. We evaluate this novel approach through simulations, under highly variable bandwidth conditions and in several multiclient scenarios. We show how the proposed approach can improve fairness up to 80% compared to state-of-the-art HAS heuristics in a scenario with three networks, each containing 30 clients streaming video at the same time. Latré, Middelheimlaan 1, B-2020 Antwerp, Belgium; emails: {jeroen.famaey, steven.latre}@uantwerpen.be. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee.

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“…We use a metric in the same range of the Mean Opinion Score (MOS), which can be computed as in Equation (1) De Vriendt et al 2013]: …”

Section: Qoe Modelmentioning

confidence: 99%

“…All the coefficients reported in Equations (1) and (2) have been fixed according to the works by De Vriendt et al [2013] and .…”

Section: Qoe Modelmentioning

confidence: 99%

QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming

Petrangeli

Famaey

Claeys

et al. 2015

ACM Trans. Multimedia Comput. Commun. Appl.

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“…De Vriendt et al [7] define the QoE of video delivered using HAS to be dependent on the average segment quality and the standard deviation of the segment quality. Using this quality level model, its parameters were tuned based on the results of a small subjective test by experts in the field of multimedia streaming.…”

Section: A Evaluation Metricmentioning

confidence: 99%

Design and Evaluation of a Self-Learning HTTP Adaptive Video Streaming Client

et al. 2014

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Abstract-HTTP Adaptive Streaming (HAS) is becoming the de facto standard for Over-The-Top (OTT)-based video streaming services such as YouTube and Netflix. By splitting a video into multiple segments of a couple of seconds and encoding each of these at multiple quality levels, HAS allows a video client to dynamically adapt the requested quality during the playout to react to network changes. However, state-of-the-art quality selection heuristics are deterministic and tailored to specific network configurations. Therefore, they are unable to cope with a vast range of highly dynamic network settings. In this letter, a novel Reinforcement Learning (RL)-based HAS client is presented and evaluated. The self-learning HAS client dynamically adapts its behaviour by interacting with the environment to optimize the Quality of Experience (QoE), the quality as perceived by the enduser. The proposed client has been thoroughly evaluated using a network-based simulator and is shown to outperform traditional HAS clients by up to 13% in a mobile network environment.

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“…The QoE model used to evaluate the proposed framework is a metric in the same range of the Mean Opinion Score (MOS), which was proposed by De Vriendt et al [23] and further improved by Claeys et al [8]. The QoE experienced by a client is a function of the average requested quality level, its standard deviation, the number of freezes and the average freeze duration.…”

Section: Performance Evaluation a Experimental Setupmentioning

confidence: 99%

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

Petrangeli

Wauters

Huysegems³

et al. 2015

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

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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.

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QoE model for video delivered over an LTE network using HTTP adaptive streaming

Cited by 61 publications

References 9 publications

QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming

QoE-Driven Rate Adaptation Heuristic for Fair Adaptive Video Streaming

Design and Evaluation of a Self-Learning HTTP Adaptive Video Streaming Client

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

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