Survey on Reinforcement Learning Approaches for Cache-Assisted Video Streaming System

Minseok Choi

doi:10.1109/ICOIN53446.2022.9687168

Survey on Reinforcement Learning Approaches for Cache-Assisted Video Streaming System

Minseok Choi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper surveys the state-of-The-Art studies on video caching and delivery especially based on the reinforcement learning (RL) approach. Even though there have been many researches on the RL-based content caching and delivery, caching and delivery of video contents should be more carefully designed to reflect the unique characteristics of video contents. We focus on the following characteristics of videos files: i) a video can be encoded into multiple quality levels, ii) provision of streaming services for mobile users is necessary, and iii) a video consists of multiple sequential chunks.

Original language	English
Title of host publication	36th International Conference on Information Networking, ICOIN 2022
Publisher	IEEE Computer Society
Pages	470-473
Number of pages	4
ISBN (Electronic)	9781665413329
DOIs	https://doi.org/10.1109/ICOIN53446.2022.9687168
Publication status	Published - 2022
Event	36th International Conference on Information Networking, ICOIN 2022 - Virtual, Jeju Island, Korea, Republic of Duration: 12 Jan 2022 → 15 Jan 2022

Publication series

Name	International Conference on Information Networking
Volume	2022-January
ISSN (Print)	1976-7684

Conference

Conference	36th International Conference on Information Networking, ICOIN 2022
Country/Territory	Korea, Republic of
City	Virtual, Jeju Island
Period	12/01/22 → 15/01/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Reinforcement learning
video streaming
wireless caching

Access to Document

10.1109/ICOIN53446.2022.9687168

Cite this

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title = "Survey on Reinforcement Learning Approaches for Cache-Assisted Video Streaming System",

abstract = "This paper surveys the state-of-The-Art studies on video caching and delivery especially based on the reinforcement learning (RL) approach. Even though there have been many researches on the RL-based content caching and delivery, caching and delivery of video contents should be more carefully designed to reflect the unique characteristics of video contents. We focus on the following characteristics of videos files: i) a video can be encoded into multiple quality levels, ii) provision of streaming services for mobile users is necessary, and iii) a video consists of multiple sequential chunks.",

keywords = "Reinforcement learning, video streaming, wireless caching",

author = "Minseok Choi",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 36th International Conference on Information Networking, ICOIN 2022 ; Conference date: 12-01-2022 Through 15-01-2022",

year = "2022",

doi = "10.1109/ICOIN53446.2022.9687168",

language = "English",

series = "International Conference on Information Networking",

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pages = "470--473",

booktitle = "36th International Conference on Information Networking, ICOIN 2022",

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}

Choi, M 2022, Survey on Reinforcement Learning Approaches for Cache-Assisted Video Streaming System. in 36th International Conference on Information Networking, ICOIN 2022. International Conference on Information Networking, vol. 2022-January, IEEE Computer Society, pp. 470-473, 36th International Conference on Information Networking, ICOIN 2022, Virtual, Jeju Island, Korea, Republic of, 12/01/22. https://doi.org/10.1109/ICOIN53446.2022.9687168

Survey on Reinforcement Learning Approaches for Cache-Assisted Video Streaming System. / Choi, Minseok.
36th International Conference on Information Networking, ICOIN 2022. IEEE Computer Society, 2022. p. 470-473 (International Conference on Information Networking; Vol. 2022-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper surveys the state-of-The-Art studies on video caching and delivery especially based on the reinforcement learning (RL) approach. Even though there have been many researches on the RL-based content caching and delivery, caching and delivery of video contents should be more carefully designed to reflect the unique characteristics of video contents. We focus on the following characteristics of videos files: i) a video can be encoded into multiple quality levels, ii) provision of streaming services for mobile users is necessary, and iii) a video consists of multiple sequential chunks.

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KW - video streaming

KW - wireless caching

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Survey on Reinforcement Learning Approaches for Cache-Assisted Video Streaming System

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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