IMPROVING CLOUD GAMING EXPERIENCE THROUGH MOBILE EDGE COMPUTING

被引:102
作者
Zhang, Xu [1 ]
Chen, Hao [8 ]
Zhao, Yangchao [1 ]
Ma, Zhan [1 ,2 ]
Xu, Yiling [4 ]
Huang, Haojun [5 ]
Yin, Hao [3 ,6 ]
Wu, Dapeng Oliver [7 ]
机构
[1] Nanjing Univ, Sch Elect Sci & Engn, Nanjing, Jiangsu, Peoples R China
[2] Nanjing Univ, Nanjing, Jiangsu, Peoples R China
[3] Shanghai Jiao Tong Univ, Cooperat Medianet Innovat Ctr, Shanghai, Peoples R China
[4] Shanghai Jiao Tong Univ, Sch Elect Informat & Elecron Engn, Shanghai, Peoples R China
[5] China Univ Geosci, Coll Comp, Beijing, Peoples R China
[6] Tsinghua Univ, RIIT, Beijing, Peoples R China
[7] Univ Florida, Dept Elect & Comp Engn, Gainesville, FL 32611 USA
[8] Shanghai Jieo Tong Univ, Shanghai, Peoples R China
基金
中国国家自然科学基金;
关键词
Reinforcement learning;
D O I
10.1109/MWC.2019.1800440
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
With the development of 4G/5G technology and smart devices, more and more users begin to play games via their mobile devices. As a promising way to enable users to play any games, cloud gaming is proposed to stream game scene rendered remotely in the cloud with the format of video. However, it faces major challenges in terms of long delay and high network bandwidth. To this end, a novel framework named EdgeGame is proposed to improve the cloud gaming experience by leveraging resources in the edge. Compared to existing cloud gaming systems, EdgeGame offloads the computation-intensive rendering to the network edge instead, which can reduce network delay and bandwidth consumption greatly. Moreover, EdgeGame introduces deep reinforcement learning in the edge to adjust the video bitrates adaptively to accommodate the network dynamics. Finally, we implemented a prototype system and compared it with an existing cloud gaming system. The experiments show that EdgeGame can reduce the average network delay by 50 percent and improve user's QoE by 20 percent.
引用
收藏
页码:178 / 183
页数:6
相关论文
共 15 条
[1]   BBR: Congestion-Based Congestion Control [J].
Cardwell, Neal ;
Cheng, Yuchung ;
Gunn, C. Stephen ;
Yeganeh, Soheil Hassas ;
Jacobson, Van .
COMMUNICATIONS OF THE ACM, 2017, 60 (02) :58-66
[2]   Analysis and Design of the Google Congestion Control for Web Real-time Communication (WebRTC) [J].
Carlucci, Gaetano ;
De Cicco, Luca ;
Holmer, Stefan ;
Mascolo, Saverio .
PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON MULTIMEDIA SYSTEMS (MMSYS'16), 2016, :133-144
[3]   GamingAnywhere: The First Open Source Cloud Gaming System [J].
Huang, Chun-Ying ;
Chen, Kuan-Ta ;
Chen, De-Yu ;
Hsu, Hwai-Jung ;
Hsu, Cheng-Hsin .
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS, 2014, 10 (01)
[4]   THE DEEP LEARNING VISION FOR HETEROGENEOUS NETWORK TRAFFIC CONTROL: PROPOSAL, CHALLENGES, AND FUTURE PERSPECTIVE [J].
Kato, Nei ;
Fadlullah, Zubair Md. ;
Mao, Bomin ;
Tang, Fengxiao ;
Akashi, Osamu ;
Inoue, Takeru ;
Mizutani, Kimihiro .
IEEE WIRELESS COMMUNICATIONS, 2017, 24 (03) :146-153
[5]  
Lumezanu C, 2009, IMC'09: PROCEEDINGS OF THE 2009 ACM SIGCOMM INTERNET MEASUREMENT CONFERENCE, P177
[6]   Neural Adaptive Video Streaming with Pensieve [J].
Mao, Hongzi ;
Netravali, Ravi ;
Alizadeh, Mohammad .
SIGCOMM '17: PROCEEDINGS OF THE 2017 CONFERENCE OF THE ACM SPECIAL INTEREST GROUP ON DATA COMMUNICATION, 2017, :197-210
[7]  
Mnih V, 2016, PR MACH LEARN RES, V48
[8]  
Riiser Haakon, 2013, P 2013 ACM MULT SYST, P114
[9]   Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges [J].
Tran, Tuyen X. ;
Hajisami, Abolfazl ;
Pandey, Parul ;
Pompili, Dario .
IEEE COMMUNICATIONS MAGAZINE, 2017, 55 (04) :54-61
[10]   A nonparametric approach to the automated protocol fingerprint inference [J].
Wang, YiPeng ;
Yun, Xiaochun ;
Zhang, Yongzheng ;
Chen, Liwei ;
Wu, Guangjun .
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2017, 99 :1-9