Computer Science ›› 2022, Vol. 49 ›› Issue (1): 194-203.doi: 10.11896/jsjkx.201100107

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented Reality

CHEN Le1, GAO Ling1,2, REN Jie3, DANG Xin1, WANG Yi-hao1, CAO Rui1, ZHENG Jie1, WANG Hai1   

  1. 1 School of Information Science and Technology,Northwest University,State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services,Xi'an 710127,China
    2 College of Computer Science State-Province,Xi'an Polytechnic University,Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services,Xi'an 710600,China
    3 School of Computer Science,Shaanxi Normal University,Xi'an 710119,China
  • Received:2020-11-16 Revised:2021-04-12 Online:2022-01-15 Published:2022-01-18
  • About author:CHEN Le,born in 1996,postgraduate.Her main research interests include mobile computing and deep learning.
    GAO Ling,born in 1964,Ph.D,professor,is a member of China Computer Federation.His main research interests include cyber security,network ma-nagement and embedded system.
  • Supported by:
    National Natural Science Foundation of China(61672552) and Science and Technology Program of Guangzhou,China(202002020045).

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.

Key words: Bitrate adaptive control, Gradient boosting regression, Mobile augmented reality, Performance optimization

CLC Number: 

  • TP391
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