Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781509015528 |
| DOIs | |
| Publication status | Published - 2016 Sep 22 |
| Event | 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016 - Seattle, United States Duration: 2016 Jul 11 → 2016 Jul 15 |
Other
| Other | 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016 |
|---|---|
| Country | United States |
| City | Seattle |
| Period | 16/7/11 → 16/7/15 |
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Keywords
- HMM
- Mobile network
- Throughput prediction
ASJC Scopus subject areas
- Signal Processing
- Media Technology
- Computer Vision and Pattern Recognition
Cite this
History-based throughput prediction with Hidden Markov Model in mobile networks. / Wei, Bo; Kanai, Kenji; Katto, Jiro.
2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7574683.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - History-based throughput prediction with Hidden Markov Model in mobile networks
AU - Wei, Bo
AU - Kanai, Kenji
AU - Katto, Jiro
PY - 2016/9/22
Y1 - 2016/9/22
N2 - 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.
AB - 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.
KW - HMM
KW - Mobile network
KW - Throughput prediction
UR - http://www.scopus.com/inward/record.url?scp=84992117684&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992117684&partnerID=8YFLogxK
U2 - 10.1109/ICMEW.2016.7574683
DO - 10.1109/ICMEW.2016.7574683
M3 - Conference contribution
AN - SCOPUS:84992117684
BT - 2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -