基于自适应码率移动增强现实应用的能效优化研究  

Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented Reality

作　　者：陈乐 高岭[1,2] 任杰 党鑫 王祎昊 曹瑞 郑杰 王海 CHEN Le;GAO Ling;REN Jie;DANG Xin;WANG Yi-hao;CAO Rui;ZHENG Jie;WANG Hai(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;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;School of Computer Science,Shaanxi Normal University,Xi'an 710119,China)

机构地区：[1]西北大学信息科学与技术学院/新型网络智能信息服务国家地方联合工程研究中心,西安710127 [2]西安工程大学计算机科学学院/新型网络智能信息服务国家地方联合工程研究中心,西安710600 [3]陕西师范大学计算机科学学院,西安710119

出　　处：《计算机科学》2022年第1期194-203,共10页Computer Science

基　　金：国家重点研发计划(2019YFC1521400);国家自然科学基金(61902229,61872294);陕西省国际科技合作计划项目(2020KW-006);中央高校基本科研业务费专项资金资助项目(GK202103084)。

摘　　要：随着移动增强现实(Mobile Augmented Reality,MAR)技术的飞速发展,MAR应用的种类及功能也越来越丰富多样,与此同时用户对MAR应用的视频质量及响应时间也提出了更高的要求。通常来说,MAR应用会将计算密集型任务(目标识别及渲染)卸载到云端或边缘服务器进行处理,并将渲染后的图像下载到移动端。但由于移动网络状态的不稳定性及网络带宽的限制,海量数据的传输将延长MAR应用响应时间,进而增加移动设备的传输能耗开销,严重影响用户使用体验。由此,文中提出了一种基于梯度提升回归(Gradient Boosting Regression,GBR)的自适应码率控制模型。该模型通过感知当前网络环境及拍摄内容,预测用户观感需求并对非关注点部分进行低码率压缩,从而在不影响用户体验的情况下尽可能地降低传输数据量,缩短响应时间。具体来说,通过分析200个热门视频的视频特征,构建视频特征同用户观感需求的内在联系,从而针对不同的用户需求提供合适的视频码率配置,由此达到维持体验、减少时延、节约能耗的目标。实验结果显示,同直接下载渲染后的1080p视频相比,提出的自适应码率控制模型在尽可能维持用户观感体验的前提下,每帧的下载时间平均减少了58%(19.13 ms)。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 bitrate 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 200popular 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(19.13ms)per frame while maintaining the user’s viewing experience as much as possible.

关 键 词：移动增强现实 能效优化 梯度提升回归 码率自适应控制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]