Enhancing Video Encoding for Cloud Virtual Reality Gaming Based on User Types

Kai Song, Haonan Sun, Junyan Huo, Fuzheng Yang, Kun Yang, Gaoxing Chen

Abstract: Cloud Virtual Reality (VR) gaming is a novel technology that allows users to enjoy complex games on their thin clients by offloading the graphics rendering to cloud servers. The thin clients only need to perform basic decoding functions, which reduces the hardware requirements and costs. However, cloud VR gaming also faces the challenge of high bandwidth consumption when transmitting high-resolution game video streams. This paper presents a cloud VR gaming system that can transmit users’ gaze point data to the server in real time to identify users’ regions of interest. With this system, we verify the difference in spatial visual sensitivity caused by the different types of users. Then, a user-type-based video encoding method is proposed. Through conducting the subjective test experiment, the proposed video encoding method can reduce the bitrate for players and viewers by at least 71% and 69%, respectively, without compromising the perceptual quality.