Abstract
Throughput prediction is one of good solutions to improve quality of mobile applications (e.g., YouTube or Netflix) for video streaming delivery services in mobile networks. This is because such applications require monitoring the network performances to control content quality, thus guarantee quality of service (QoS) and quality of experience (QoE). In this paper, we propose a history-based TCP throughput prediction method incorporating communication quality features using SVR (Support Vector Regression). By taking history of communication quality features such as historical throughput and Received Signal Strength Indication (RSSI) into consideration, the throughput prediction error can be decreased. We conduct experiments with the proposed method and compare the prediction accuracy with a variety of methods in different scenarios of various moving modes of users. Results show that the proposed model could predict throughput effectively in various scenarios and decrease throughput prediction errors by a maximum of 26.47% compared with other methods.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 374-375 |
| Number of pages | 2 |
| Volume | 2017-January |
| ISBN (Electronic) | 9781538629369 |
| DOIs | |
| Publication status | Published - 2017 Dec 28 |
| Event | 19th IEEE International Symposium on Multimedia, ISM 2017 - Taichung, Taiwan, Province of China Duration: 2017 Dec 11 → 2017 Dec 13 |
Other
| Other | 19th IEEE International Symposium on Multimedia, ISM 2017 |
|---|---|
| Country | Taiwan, Province of China |
| City | Taichung |
| Period | 17/12/11 → 17/12/13 |
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Keywords
- mobile network
- support vector regression
- throughput prediction
ASJC Scopus subject areas
- Media Technology
- Sensory Systems
Cite this
A History-Based TCP Throughput Prediction Incorporating Communication Quality Features by Support Vector Regression for Mobile Network. / Wei, Bo; Kawakami, Wataru; Kanai, Kenji; Katto, Jiro.
Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 374-375.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A History-Based TCP Throughput Prediction Incorporating Communication Quality Features by Support Vector Regression for Mobile Network
AU - Wei, Bo
AU - Kawakami, Wataru
AU - Kanai, Kenji
AU - Katto, Jiro
PY - 2017/12/28
Y1 - 2017/12/28
N2 - Throughput prediction is one of good solutions to improve quality of mobile applications (e.g., YouTube or Netflix) for video streaming delivery services in mobile networks. This is because such applications require monitoring the network performances to control content quality, thus guarantee quality of service (QoS) and quality of experience (QoE). In this paper, we propose a history-based TCP throughput prediction method incorporating communication quality features using SVR (Support Vector Regression). By taking history of communication quality features such as historical throughput and Received Signal Strength Indication (RSSI) into consideration, the throughput prediction error can be decreased. We conduct experiments with the proposed method and compare the prediction accuracy with a variety of methods in different scenarios of various moving modes of users. Results show that the proposed model could predict throughput effectively in various scenarios and decrease throughput prediction errors by a maximum of 26.47% compared with other methods.
AB - Throughput prediction is one of good solutions to improve quality of mobile applications (e.g., YouTube or Netflix) for video streaming delivery services in mobile networks. This is because such applications require monitoring the network performances to control content quality, thus guarantee quality of service (QoS) and quality of experience (QoE). In this paper, we propose a history-based TCP throughput prediction method incorporating communication quality features using SVR (Support Vector Regression). By taking history of communication quality features such as historical throughput and Received Signal Strength Indication (RSSI) into consideration, the throughput prediction error can be decreased. We conduct experiments with the proposed method and compare the prediction accuracy with a variety of methods in different scenarios of various moving modes of users. Results show that the proposed model could predict throughput effectively in various scenarios and decrease throughput prediction errors by a maximum of 26.47% compared with other methods.
KW - mobile network
KW - support vector regression
KW - throughput prediction
UR - http://www.scopus.com/inward/record.url?scp=85045831011&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045831011&partnerID=8YFLogxK
U2 - 10.1109/ISM.2017.74
DO - 10.1109/ISM.2017.74
M3 - Conference contribution
VL - 2017-January
SP - 374
EP - 375
BT - Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -