QOE-Based Neural Live Streaming Method with Continuous Dynamic Adaptive Video Quality Control

In this paper, a quality of experience (QoE)-based neural live streaming method with dynamic adaptive video quality control is developed to improve streaming performance. First, the dynamic adaptive streaming issue is formulated as a Markov decision process (MDP) problem. Second, an reinforcement learning (RL)-based approach is proposed as an appropriate solution, where the client functions as an RL agent and the environment is made up of various networks. User QoE is the reward by mutual consideration of video quality and play-back state. Finally, to optimize the total reward, the RL algorithm chooses the required video quality for each video segment. Experimental results show that the proposed RL-based streaming algorithm outperforms state-of-the-art schemes in terms of both temporal and visual QoE metrics by a noticeable margin while guaranteeing application-level fairness when multiple clients share a bottlenecked network. The code is available on the following website: https://github.com/OpenCode007/ICME2021.