Reinforcement Learning Based Dynamic Adaptive Video Streaming for Multi-client over NDN

Abstract

The performance of Dynamic Adaptive Streaming (DAS) in multi-client scenarios can be improved by taking advantage of the aggregation capability of Named Data Networking (NDN). In this paper, we propose a client-side reinforcement learning based (RL) ABR algorithm for NDN that can achieve proactive aggregation of requests among clients as much as possible without requiring coordinating with other clients or scheduling by a central controller. We model the interaction process between the DAS client and the network as a Markov decision process. Then, the appropriate states and rewards are selected to decide on the Markov decision process through the reinforcement learning algorithm. Through constant training, the reinforcement learning algorithm is able to guide the client to request the same video bitrate, namely request aggregation, thereby reducing repetitive traffic and achieving fairness. Compared with the existing solutions, through experiments in multi-client video distribution scenarios, the RL algorithm performs well in the overall Quality of Experience (QoE), fairness, and aggregation rate, etc.