Adaptive Streaming Scheme with Reinforcement Learning in Edge Computing Environments

DASH is an effective way to improve the Quality of Experience (QoE) of video streaming. However, existing schemes lack consideration for a multi-client environment, so QoE fairness deteriorates when multiple clients stream video through the same network. In this paper, we propose an adaptive streaming scheme with reinforcement learning in edge computing environments. The proposed scheme learns policy based on reinforcement learning to improve QoE and uses edge computing to improve QoE fairness. We evaluated the performance of the proposed scheme through simulation-based experiments under various network conditions. Through the experimental results, we confirmed that the proposed scheme achieves better performance than the existing schemes in a multi-client environment.