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

Suphakit Awiphan, Jakramate Bootkrajang, Kanin Poobai, Jiro Katto

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

Abstract

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.

Original languageEnglish
Title of host publication2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages99-100
Number of pages2
ISBN (Electronic)9781665436762
DOIs
Publication statusPublished - 2021
Event10th IEEE Global Conference on Consumer Electronics, GCCE 2021 - Kyoto, Japan
Duration: 2021 Oct 122021 Oct 15

Publication series

Name2021 IEEE 10th Global Conference on Consumer Electronics, GCCE 2021

Conference

Conference10th IEEE Global Conference on Consumer Electronics, GCCE 2021
Country/TerritoryJapan
CityKyoto
Period21/10/1221/10/15

Keywords

  • adaptive video streaming
  • named data networking
  • reinforcement learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing
  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation

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