Video encoding adaptation for QoE maximization over 5G cellular networks

Abstract

With the unprecedented growth of mobile video data traffic every year, increasing users' video quality of experience (QoE) under limited network radio resources becomes a critical issue in next-generation cellular systems. However, videos compressed with unsuitable video encoding parameters will waste the network radio resources and users even may not be able to get satisfied video QoE. Specifically, a video with higher bit rate requirement (determined by the video encoding parameters) does not imply that a user will have better QoE. This paper studies the QoE optimization problem via carefully determining a combination of video encoding parameters for each user under the radio resource block constraint. The objective is to maximize the total QoE of all users. We prove that our target problem is NP-hard and propose an algorithm based on dynamic programming to solve the problem. Then, we prove that our proposed algorithm is a pseudo-polynomial time optimal algorithm. We construct a series of simulations with realistic video sequences encoded by H.264 and network settings to evaluate the performance of our proposed algorithm. Compared with two baselines, the simulation results show that the proposed algorithm can significantly improve the total QoE of all users and indicate that the video encoding adaptation is an important issue in the QoE optimization problem.