Q-learning based control algorithm for HTTP adaptive streaming

Martín, Virginia; Cabrera, Julián; García, Narciso

doi:10.1109/vcip.2015.7457906

Cited by 4 publications

(2 citation statements)

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“…Another promising solution for optimizing the Quality of Experience of the mobile user is the integration of reinforcement learning algorithms into the multimedia adaptation process [117], [118], [119], [120]. The works in [117] and [118] make use of Q-Learning approaches to design a HAS client.…”

Section: E Discussionmentioning

confidence: 99%

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Seamless Multimedia Delivery Within a Heterogeneous Wireless Networks Environment: Are We There Yet?

Trestian

Comşa

Tüysüz

2018

IEEE Commun. Surv. Tutorials

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Copyright and moral rights to this work are retained by the author and/or other copyright owners unless otherwise stated. The work is supplied on the understanding that any use for commercial gain is strictly forbidden. A copy may be downloaded for personal, non-commercial, research or study without prior permission and without charge.Works, including theses and research projects, may not be reproduced in any format or medium, or extensive quotations taken from them, or their content changed in any way, without first obtaining permission in writing from the copyright holder(s). They may not be sold or exploited commercially in any format or medium without the prior written permission of the copyright holder(s).Full bibliographic details must be given when referring to, or quoting from full items including the author's name, the title of the work, publication details where relevant (place, publisher, date), pagination, and for theses or dissertations the awarding institution, the degree type awarded, and the date of the award.If you believe that any material held in the repository infringes copyright law, please contact the Repository Team at Middlesex University via the following email address:eprints@mdx.ac.ukThe item will be removed from the repository while any claim is being investigated. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Abstract-The increasing popularity of live video streaming from mobile devices such as Facebook Live, Instagram Stories, Snapchat, etc. pressurises the network operators to increase the capacity of their networks. However, a simple increase in system capacity will not be enough without considering the provisioning of Quality of Experience (QoE) as the basis for network control, customer loyalty and retention rate and thus increase in network operators revenue. As QoE is gaining strong momentum especially with increasing users' quality expectations, the focus is now on proposing innovative solutions to enable QoE when delivering video content over heterogeneous wireless networks. In this context, this paper presents an overview of multimedia delivery solutions, identifies the problems and provides a comprehensive classification of related state-of-theart approaches following three key directions: adaptation, energy efficiency and multipath content delivery. Discussions, challenges and open issues on the seamless multimedia provisioning faced by the current and next generation of wireless networks are also provided.

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Section: E Discussionmentioning

confidence: 99%

“…The works in [117] and [118] make use of Q-Learning approaches to design a HAS client. Based on the current network conditions, the HAS client dynamically learns the optimal behaviour to optimize the users' QoE.…”

Section: E Discussionmentioning

confidence: 99%

Seamless Multimedia Delivery Within a Heterogeneous Wireless Networks Environment: Are We There Yet?

Trestian

Comşa

Tüysüz

2018

IEEE Commun. Surv. Tutorials

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Adaptive streaming algorithm based on reinforcement learning

Feng

Wang

Jiang

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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As video streaming occupies a large part of the Internet traffic, and DASH gradually becomes the dominant standard for video transmission recent years, a lot of researches about client-side bitrate adaptive algorithms are constantly emerging.This paper mainly adopts the DRL(Deep Reinforcement learning) algorithm that combines Deep Learning and Reinforcement Learning techniques to optimize the Quality of Experience (QoE) of DASH.This method learns to make decisions by observing the network environment and adjust the strategy to get more reward based on feedback,which doesn’t rely on pre-programmed models or assumptions about the environment. After experiments on the real network trace datasets, we can find that the algorithm this paper adopted can obtain higher quality of user experience than the existing researches.Moreover, the experimental results show that less buffer underflow occurs and smoother video playback is guaranteed.

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