A History-Based TCP Throughput Prediction Incorporating Communication Quality Features by Support Vector Regression for Mobile Network

Bo Wei, Wataru Kawakami, Kenji Kanai, Jiro Katto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages374-375
Number of pages2
Volume2017-January
ISBN (Electronic)9781538629369
DOIs
Publication statusPublished - 2017 Dec 28
Event19th IEEE International Symposium on Multimedia, ISM 2017 - Taichung, Taiwan, Province of China
Duration: 2017 Dec 112017 Dec 13

Other

Other19th IEEE International Symposium on Multimedia, ISM 2017
CountryTaiwan, Province of China
CityTaichung
Period17/12/1117/12/13

Fingerprint

Wireless networks
History
Throughput
Communication
Webcasts
Mobile Applications
Quality Control
Video streaming
Network performance
Quality of service
Monitoring
Experiments

Keywords

  • mobile network
  • support vector regression
  • throughput prediction

ASJC Scopus subject areas

  • Media Technology
  • Sensory Systems

Cite this

Wei, B., Kawakami, W., Kanai, K., & Katto, J. (2017). A History-Based TCP Throughput Prediction Incorporating Communication Quality Features by Support Vector Regression for Mobile Network. In Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017 (Vol. 2017-January, pp. 374-375). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISM.2017.74

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 proceedingConference contribution

Wei, B, Kawakami, W, Kanai, K & Katto, J 2017, A History-Based TCP Throughput Prediction Incorporating Communication Quality Features by Support Vector Regression for Mobile Network. in Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 374-375, 19th IEEE International Symposium on Multimedia, ISM 2017, Taichung, Taiwan, Province of China, 17/12/11. https://doi.org/10.1109/ISM.2017.74
Wei B, Kawakami W, Kanai K, Katto J. A History-Based TCP Throughput Prediction Incorporating Communication Quality Features by Support Vector Regression for Mobile Network. In Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017. Vol. 2017-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 374-375 https://doi.org/10.1109/ISM.2017.74
Wei, Bo ; Kawakami, Wataru ; Kanai, Kenji ; Katto, Jiro. / A History-Based TCP Throughput Prediction Incorporating Communication Quality Features by Support Vector Regression for Mobile Network. Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 374-375
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