History-based throughput prediction with Hidden Markov Model in mobile networks

研究成果: Conference contribution

6 引用 (Scopus)

抜粋

Throughput prediction contributes a lot to adaptive bitrate control, adjusting the quality of video streaming accordingly to offer smooth media transmission and save energy at the same time. To solve the problem of throughput prediction for real time communication, this paper puts forward a new history-based throughput prediction method applying Hidden Markov Model in mobile networks. The main purpose of this method is to predict future throughput for real time communication in mobile network. Our novel approach utilizes Hidden Markov Model (HMM) with Gaussian Mixture Model (GMM) to deal with history time series of throughput and judge fluctuation factor with total variance when predicting future throughput. By conducting experiments with the new methodology, we compare the accuracy of the proposed method with three other conventional prediction methods. Results show our proposed method could identify data fluctuation effectively and predict future 100s throughput with high accuracy in various situations.

元の言語English
ホスト出版物のタイトル2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
出版者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781509015528
DOI
出版物ステータスPublished - 2016 9 22
イベント2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016 - Seattle, United States
継続期間: 2016 7 112016 7 15

Other

Other2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
United States
Seattle
期間16/7/1116/7/15

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition

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  • これを引用

    Wei, B., Kanai, K., & Katto, J. (2016). History-based throughput prediction with Hidden Markov Model in mobile networks. : 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016 [7574683] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMEW.2016.7574683