Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented Reality

Abstract

With the development of the mobile augmented reality (MAR),users have higher requirements on video quality and response time on it.MAR applications offload computation-intensive tasks to the cloud or edge servers for processing.In order to provide users with high-quality rendering services,MAR needs to download massive amounts of data from cloud or edge servers.Due to the instability of network condition and the limitation of network bandwidth,data transmission will extend MAR application response time,which increases the energy consumption,and seriously affects the user experience.This paper proposes a bit-rate adaptive model based on gradient boosting regression (GBR).The model considers the different needs of users in different network conditions,analyzes the features of the 200 popular videos,finds the connection between the video features and the user requirements,and provides appropriate video bitrate configuration according to different needs,thus to achieve the goal of maintaining experience,reducing latency and saving energy.The results show that compared with the original rendered 1080p video,the proposed bitrate adaptive model can save 58% downloading time latency(19.13 ms) per frame while maintaining the user's viewing experience as much as possible.

Keywords

• mobile augmented reality|performance optimization|gradient boosting regression|bitrate adaptive control