Live Video Streaming Optimization Based on Deep Reinforcement Learning

Video players employ adaptive bitrate algorithms in video-on-demand (VoD) scenarios to improve user-perceived quality of experience (QoE), whereas performance will obviously decline in live video streaming scenarios. To this end, we propose a novel deep reinforcement learning (DRL) based live video streaming optimization approach. Firstly, we point out the optimization objectives by comparing the difference between the VoD scenario and the live video streaming scenario. Then, according to the optimization conditions, we establish QoE optimization model in combination with a state-of-the-art DRL algorithm. We compare our algorithm with state-of-the-art ABR algorithms in a simulator with real-world video and network trace. Simulation results show that the proposed algorithm improves user experience quality by 5.6% on average, compared with existing algorithms.