Computer Science ›› 2018, Vol. 45 ›› Issue (10): 124-129.doi: 10.11896/j.issn.1002-137X.2018.10.024

• Network & Communication • Previous Articles     Next Articles

Sensor-based Adaptive Rate Control Method for Mobile Streaming

XIONG Li-rong, YOU Ri-jing, JIN Xin   

  1. College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2018-03-18 Online:2018-11-05 Published:2018-11-05

Abstract: Mobile terminal video streaming service has been paid more and more attention,and intelligent terminal rate adaptation mechanism has become a research hotspot.It costs plenty of network traffic for the mobile users to watch high-definition movies.When users are not interested in the movies,or the terminals are far away from users,watching HD movies will not bring good experience and will waste a lot of wireless network traffic.This paper designed a sensor-based rate adaptive decision model,taking into account the users’ watching location,interests and equipment status.The sensor-based rate adaptive decision model can optimize the tranditional rate decision mechanism for designing sesor-based hybrid rate decision method.The experiments show that the proposed sensor-based rate adaption model can effectively save the wireless network bandwidth resources in the case of insufficient network bandwidth.

Key words: Mobile streaming media transmission, Multi-objective optimization, Rate adaption mechanism, Sensor

CLC Number: 

  • TP311
[1]中国互联网络信息中心.第38次中国互联网络发展状况统计报告[EB/OL].http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201608/P020160803367337470363.pdf.
[2]Dynamic Adaptive Streaming over HTTP[EB/OL].https://en.wikipedia.org/wiki/Dynamic_Adaptive_Streaming_over_http,2012.
[3]HESSE S.Design of scheduling and rate-adaptation algorithms for adaptive HTTP streaming[C]∥SPIE Optical Engineering+Applications.2013:88560M.
[4]MLLER C,LEDERER S,TIMMERER C.An evaluation of dynamic adaptive streaming over HTTP in vehicular environments[C]∥Proceedings of the 4th Workshop on Mobile Video.ACM,2012:37-42.
[5]MUELLER C,LEDERER S,GRANDL R,et al.Oscillation compensating Dynamic Adaptive Streaming over HTTP[C]∥IEEE International Conference on Multimedia and Expo.IEEE,2015:1-6.
[6]CLAEYS M,LAT S,FAMAEY J,et al.Design of a Q-lear- ning-based Client Quality Selection Algorithm for HTTP Adaptive Video Streaming[C]∥Adaptive and Learning Agents Workshop.Saint-Paul,USA,2013:30-37. [7]CLAEYS M,LATR S,FAMAEY J,et al.Design and Evaluation of A Self-learning HTTP Adaptive Video Streaming Client[J].IEEE Communications Letters,2014,18(4):716-719.
[8]CHIARIOTTI F.Reinforcement learning algorithms for DASH video streaming[D].Padova:University of Padova,2015.
[9]CHIARIOTTI F,D’ARONCO S,TONI L,et al.Online learning adaptation strategy for DASH clients[C]∥International Conference on Multimedia Systems.ACM,2016:8.
[10]WILK S,SCHNHERR S,STOHR D,et al.EnvDASH:An Environment-Aware Dynamic Adaptive Streaming over HTTP System[C]∥Proceedings of the ACM International Conference on Interactive Experiences for TV and Online Video.ACM,2015:113-118.
[11]JAGANNATH A.Implementation and Analysis of User Adaptive Mobile Video Streaming using MPEG-DASH[D].ArLington:University of Texas at Arlington,2014.
[12]REZNIK Y A.User-adaptive mobile video streaming using MPEG-DASH[C]∥Proceedings of SPIE-The International Society for Optical Engineerng.2013:88560J-88560J-5.
[13]SMITH B A,YIN Q,FEINER S K,et al.Gaze locking:passive eye contact detection for human-object interaction[C]∥Proceedings of the 26th annual ACM symposium on User interface software and technology.ACM,2013:271-280.
[14]BUI D H,LIU Y,KIM H,et al.Rethinking Energy-Performance Trade-Off in Mobile Web Page Loading[C]∥Proceedings of the 21st Annual International Conference on Mobile Computing and Networking.ACM,2015:14-26.
[15]YOUNG J G,TRUDEAU M,ODELL D,et al.Touch-screen tablet user configurations and case-supported tilt affect head and neck flexion angles[J].Work,2012,41(1):81-91.
[16]WILK S,KOPF S,EFFELSBERG W.Video composition by the crowd:a system to compose user-generated videos in near real-time[C]∥Proceedings of the 6th ACM Multimedia Systems Conference.ACM,2015:13-24.
[17]WILK S,EFFELSBERG W.The influence of camera shakes, harmful occlusions and camera misalignment on the perceived quality in user generated video[C]∥2014 IEEE International Conference on Multimedia and Expo (ICME).IEEE,2014:1-6.
[18]MUELLER C,LEDERER S,POECHER J,et al.Demo paper:Libdash-an open source software library for the mpeg-dash standard[C]∥IEEE Multimedia and Expo Workshops (ICMEW).California,USA,2013:1-2.
[19]ASSEMBLY ITU.Methodology for the subjective assessment of the quality of television pictures[M].International Telecommunication Union,2003.
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