Reinforcement Learning Based Adaptive Video Streaming on Named Data Networking

Suphakit Awiphan, Jakramate Bootkrajang, Jiro Katto

研究成果: Conference contribution

抄録

Under complex network conditions, adaptive video streaming requires additional state information for optimal quality selection. In this paper, we present the applicability of reinforcement learning techniques on NDN adaptive streaming. Both buffer-based and throughput-based adaptation are studied and observed their characteristics. The Q-learning algorithm is used to learn state-action values. Based on a greedy policy, the simulation results demonstrate that RL agents tend to choose the best possible bitrate which consequently reduces the quality fluctuation in adaptive streaming.

本文言語English
ホスト出版物のタイトル2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ405-406
ページ数2
ISBN(電子版)9781728198026
DOI
出版ステータスPublished - 2020 10 13
イベント9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan
継続期間: 2020 10 132020 10 16

出版物シリーズ

名前2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

Conference9th IEEE Global Conference on Consumer Electronics, GCCE 2020
国/地域Japan
CityKobe
Period20/10/1320/10/16

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学
  • メディア記述
  • 器械工学
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識

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