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Reinforcement Learning - based Adaptation and Scheduling Methods for Multi-source DASH

Nghia T. Nguyen^{1, 2}, Long Luu^{1, 2}, Phuong L. Vo^{1, 2}, Thi Thanh Sang Nguyen^{1, 2}, Cuong T. Do³ and Ngoc-thanh Nguyen⁴

1. School of Computer and Engineering, International University
   Ho Chi Minh City, Vietnam
2. Vietnam National University
   Ho Chi Minh City, Vietnam
   {ntnghia, vtlphuong, nttsang}@hcmiu.edu.vn, ITITIU18079@student.hcmiu.edu.vn
3. Department of Computer Engineering, Kyung Hee University
   446-701, Korea
   dtcuong@khu.ac.kr, dothecuong@gmail.com
4. Wroclaw University of Science and Technology
   Poland
   ngoc-thanh.nguyen@pwr.edu.pl

Abstract

Dynamic adaptive streaming over HTTP (DASH) has been widely used in video streaming recently. In DASH, the client downloads video chunks in order from a server. The rate adaptation function at the video client enhances the user’s quality-of-experience (QoE) by choosing a suitable quality level for each video chunk to download based on the network condition. Today networks such as content delivery networks, edge caching networks, contentcentric networks, etc. usually replicate video contents on multiple cache nodes. We study video streaming from multiple sources in this work. In multi-source streaming, video chunks may arrive out of order due to different conditions of the network paths. Hence, to guarantee a high QoE, the video client needs not only rate adaptation, but also chunk scheduling. Reinforcement learning (RL) has emerged as the state-of-the-art control method in various fields in recent years. This paper proposes two algorithms for streaming from multiple sources: RL-based adaptation with greedy scheduling (RLAGS) and RL-based adaptation and scheduling (RLAS). We also build a simulation environment for training and evaluation. The efficiency of the proposed algorithms is proved via extensive simulations with real-trace data.

Key words

multi-source streaming, reinforcement learning, proximal policy optimization, dynamic adaptation streaming over HTTP

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS220927055N

Publication information

Volume 20, Issue 1 (January 2023)
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Nguyen, N. T., Luu, L., Vo, P. L., Nguyen, T. T. S., Do, C. T., Nguyen, N.: Reinforcement Learning - based Adaptation and Scheduling Methods for Multi-source DASH. Computer Science and Information Systems, Vol. 20, No. 1, 157–173. (2023), https://doi.org/10.2298/CSIS220927055N