Publication: Quality Optimization of Live Streaming ...
Master data
Title: | Quality Optimization of Live Streaming Services over HTTP with Reinforcement Learning |
Subtitle: | |
Abstract: | Recent years have seen tremendous growth in HTTP adaptive live video traffic over the Internet. In the presence of highly dynamic network conditions and diverse request patterns, existing yet simple hand-crafted heuristic approaches for serving client requests at the network edge might incur a large overhead and significant increase in time complexity. Therefore, these approaches might fail in delivering acceptable Quality of Experience (QoE) to end users. To bridge this gap, we propose ROPL, a learning-based client request management solution at the edge that leverages the power of the recent breakthroughs in deep reinforcement learning, to serve requests of concurrent users joining various HTTP-based live video channels. ROPL is able to react quickly to any changes in the environment, performing accurate decisions to serve clients requests, which results in achieving satisfactory user QoE. We validate the efficiency of ROPL through trace-driven simulations and a real-world setup. Experimental results from real-world scenarios confirm that ROPL outperforms existing heuristic-based approaches in terms of QoE, with a factor up to 3.7×. |
Keywords: | Network Edge, Request Serving, HTTP Live Streaming, Low Latency, QoE, Deep Reinforcement Learning |
Publication type: | Article in Proceedings (Authorship) |
Publication date: | 12.2021 (Print) |
Published by: |
GLOBECOM '21 Proceedings of the IEEE Global Communications Conference
GLOBECOM '21 Proceedings of the IEEE Global Communications Conference
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IEEE;
)
to publication |
Title of the series: | - |
Volume number: | - |
First publication: | Yes |
Version: | - |
Page: | pp. 1 - 6 |
Versionen
Keine Version vorhanden |
Publication date: | 12.2021 |
ISBN: |
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ISSN: | - |
Homepage: | https://ieeexplore.ieee.org/document/9685933 |
Publication date: | 02.02.2022 |
ISBN (e-book): | - |
eISSN: | - |
DOI: | http://dx.doi.org/10.1109/globecom46510.2021.9685933 |
Homepage: | https://ieeexplore.ieee.org/document/9685933 |
Open access |
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Authors
Farzad Tashtarian (internal) | ||||
Reyhane Falanji
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Abdelhak Bentaleb
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Alireza Erfanian (internal) | ||||
Peyman Sheikholharam Mashhadi (external) | ||||
Christian Timmerer (internal) | ||||
Hermann Hellwagner (internal) | ||||
Roger Zimmermann (external) |
Assignment
Organisation | Address | ||||
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Fakultät für Technische Wissenschaften
Institut für Informationstechnologie
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AT - 9020 Klagenfurt am Wörthersee |
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Peer reviewed |
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Cooperations
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Sharif University of Technology
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IR Tehran |
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National University of Singapore
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SG - 119077 Singapur |
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Halmstad University
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SE - 301 18 Halmstad |
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