Reinforcement learning - based adaptation and scheduling methods for multi-source DASH

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, 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.