Mitigation of Cold Start Problem in Experience-Based Adaptive Streaming over NDN

Suphakit Awiphan, Jakramate Bootkrajang, Kanin Poobai, Jiro Katto

研究成果: Conference contribution

抄録

Dynamic adaptive streaming over NDN typically relies on past information of network conditions and streaming quality. In this paper, we address the cold start problem associated with reinforcement learning based NDN adaptive streaming where a new consumer often found choosing bitrate arbitrarily due to the lack of experience. The idea is to construct a shared Q-Table which is continuously updated by previous consumers. Based on this Q-Table, a new consumer is expected to start choosing the segment bitrate more proactively. Simulations through ns-3 show that the proposed approach could help the consumers to find an optimal action from the beginning of the session.

本文言語English
ホスト出版物のタイトル2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
出版社Institute of Electrical and Electronics Engineers Inc.
ページ99-100
ページ数2
ISBN(電子版)9781665436762
DOI
出版ステータスPublished - 2021
イベント10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan
継続期間: 2021 10月 122021 10月 15

出版物シリーズ

名前2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference10th IEEE Global Conference on Consumer Electronics, GCCE 2021
国/地域Japan
CityKyoto
Period21/10/1221/10/15

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 信号処理
  • 生体医工学
  • 電子工学および電気工学
  • メディア記述
  • 器械工学

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