Computer Science ›› 2019, Vol. 46 ›› Issue (3): 103-107.doi: 10.11896/j.issn.1002-137X.2019.03.014

• ChinaMM2018 • Previous Articles     Next Articles

Profit Optimization for Multi-content Video Streaming over Mobile Network Based on User Preference

XU Jing-ce1,2,3,LIANG Bing1,2,3,LI Meng-nan1,2,3,JI Wen1,3,CHEN Yi-qiang1,2,3   

  1. (Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190,China)1
    (University of Chinese Academy Sciences,Beijing 100190,China)2
    (Beijing Key Laboratory of Mobile Computing and Pervasive Device,Beijing 100190,China)3
  • Received:2018-07-11 Revised:2018-09-21 Online:2019-03-15 Published:2019-03-22

Abstract: In recent years,the emergence of 4G and 5G network has greatly improved the bandwidth of mobile device data transmission,while the performance of video playback devices has been also improved,which increases the user’s demand on the quality of video streaming gradually.Thus,improving the profit of video streaming over mobile network is becoming more and more important.This paper analyzed the effect of user preference on the profit of multi-content videostreaming system.Moreover,this paper proposed the profit model of End Users based on user preference by consi-dering the traffic cost and formulated the optimization problem of total system profit into weighted profit optimization problem.Considering that the users with different preferences have different effects on the total profit of video streaming system,this paper proposed a weight selection algorithm of End Users based on preference-bitrate ratio to select the optimal weights under the condition of current user preferences.Then the optimal bitrate under the condition of current user preference was obtained by solving the optimization problem of optimal weighted profit of End Users.The experimental results show that the proposed method improves the total profit of system by 5%~10% compared with the exis-ting method.

Key words: Multi-content, Preference-bitrate ratio, Profit, Traffic cost, User preference, Video transmission

CLC Number: 

  • TP305
[1]Networking Index C V.Forecast and methodology,2016-2021,white paper.http://www.cisco.com/c/en/us/solutions/collateral/service-provider/global-cloud-index-gcl/white-paper-c11-738085.html.
[2]DERNBACH S,TAFT N,KUROSE J,et al.Cache content-selection policies for streaming video services[C]∥35th Annual IEEE International Conference on Computer Communications.IEEE,2016:1-9.
[3]LEE M C,MOLISCH A F,SASTRY N,et al.Individual Pre-
ference Probability Modeling for Video Content in Wireless Ca-ching Networks[C]∥IEEE Global Communications Confe-rence.IEEE,2017:1-7.
[4]RODRGUEZ D Z,ROSA R L,COSTA E A,et al.Video quality
assessment in video streaming services considering user pre-ference for video content[J].IEEE Transactions on Consumer Electronics,2014,60(3):436-444.
[5]WU H,LIU L,ZHANG X,et al.Quality of video oriented pricing incentive for mobile video offloading[C]∥I35th Annual IEEE International Conference on Computer Communications.IEEE,2016:1-9.
[6]JOE-WONG C,HA S,CHIANG M.Sponsoring mobile data:An economic analysis of the impact on users and content providers[C]∥2015 IEEE Conference on Computer Communications (INFOCOM).IEEE,2015:1499-1507.
[7]XIE J,XIE R,HUANG T,et al.Energy-efficient cache resource allocation and QoE optimization for HTTP adaptive bit rate streaming over cellular networks[C]∥2017 IEEE International Conference on Communications (ICC).IEEE,2017:1-6.
[8]STREIJL R C,WINKLER S,HANDS D S.Mean opinion score (MOS) revisited:methods and applications,limitations and alternatives[J].Multimedia Systems,2016,22(2):213-227.
[9]HARPER F M,KONSTAN J A.The movielens datasets:History and context[J].ACM Transactions on Interactive Intelligent Systems (TiiS),2016,5(4):19.
[10]JI W,FROSSARD P,CHEN B W,et al.Profit optimization for wireless video broadcasting systems based on polymatroidal analysis[J].IEEE Transactions on Multimedia,2015,17(12):2310-2327.
[11]COVER T M,THOMAS J A.Elements of information theory[M].New Jersey:John Wiley & Sons Inc.,2012:272-275.
[12]JOSEPH V,VECIANA G.NOVA:QoE-driven optimization of
DASH-base video delivery in networks[C]∥INFOCOM,2014 Proceedings IEEE.IEEE,2014:82-90.
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