A Video Adaptive Bitrate Algorithm with User QoE Prediction as Reward

This paper proposes a deep learning-based user QoE prediction network (UQPN)). In this work, the current user's QoE is predicted and modeled based on the current video playback states, and UQPN is used to replace the existing reward functions, in this way the generated ABR algorithm can make bitrate decisions more in line with user requirements. Experiments and the comparison with the existing reward functions show that he correlation coefficient of UQPN prediction and user QoE is higher, and the algorithm using UQPN as reinforcement learning reward can improve user QoE by at least 20%.