Throughput Prediction Using Recurrent Neural Network Model

Bo Wei, Mayuko Okano, Kenji Kanai, Wataru Kawakami, Jiro Katto

研究成果: Conference contribution

抜粋

To ensure good quality of experience for user when transmitting video content, throughput prediction can contribute to the selection of proper bitrate. In this paper, we propose a throughput prediction method with recurrent neural network (RNN) model. Experiments are conducted to evaluate the methods, and the results indicate that proposed method can decrease the prediction error by a maximum of 29.39% compared with traditional methods.

元の言語English
ホスト出版物のタイトル2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018
出版者Institute of Electrical and Electronics Engineers Inc.
ページ88-89
ページ数2
ISBN(電子版)9781538663097
DOI
出版物ステータスPublished - 2018 12 12
イベント7th IEEE Global Conference on Consumer Electronics, GCCE 2018 - Nara, Japan
継続期間: 2018 10 92018 10 12

出版物シリーズ

名前2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018

Other

Other7th IEEE Global Conference on Consumer Electronics, GCCE 2018
Japan
Nara
期間18/10/918/10/12

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Instrumentation

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  • これを引用

    Wei, B., Okano, M., Kanai, K., Kawakami, W., & Katto, J. (2018). Throughput Prediction Using Recurrent Neural Network Model. : 2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018 (pp. 88-89). [8574877] (2018 IEEE 7th Global Conference on Consumer Electronics, GCCE 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCCE.2018.8574877