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

• URL: http://arxiv.org/abs/2308.11621v1
• Date: Tue, 25 Jul 2023 06:47:12 GMT
• Title: Reinforcement Learning -based Adaptation and Scheduling Methods for Multi-source DASH
• Authors: Nghia T. Nguyen, Long Luu, Phuong L. Vo, Thi Thanh Sang Nguyen, Cuong T. Do, Ngoc-thanh Nguyen
• Abstract summary: Dynamic adaptive streaming over HTTP (DASH) has been widely used in video streaming recently. In multi-source streaming, video chunks may arrive out of order due to different conditions of the network paths. This paper proposes two algorithms for streaming from multiple sources: RL-based adaptation with greedy scheduling (RLAGS) and RL-based adaptation and scheduling (RLAS)
• Score: 1.1971219484941955
• License: http://creativecommons.org/licenses/by/4.0/
• 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, content-centric networks,... 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 evaluating. The efficiency of the proposed algorithms is proved via extensive simulations with real-trace data.

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