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

TL;DR

This paper introduces RL-based algorithms for adaptive video streaming from multiple sources, addressing out-of-order chunk arrivals and optimizing QoE through rate adaptation and scheduling, validated by extensive simulations.

Contribution

It proposes two novel RL-based algorithms, RLAGS and RLAS, for multi-source DASH streaming, combining rate adaptation with chunk scheduling.

Findings

RL algorithms outperform traditional methods in QoE metrics

Proposed methods effectively handle out-of-order chunks

Simulation results with real data validate efficiency

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.