Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/71418
Title: Reinforcement Learning Based Adaptive Video Streaming on Named Data Networking
Authors: Suphakit Awiphan
Jakramate Bootkrajang
Jiro Katto
Keywords: Computer Science
Engineering
Physics and Astronomy
Issue Date: 13-Oct-2020
Abstract: © 2020 IEEE. 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.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85099407952&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/71418
Appears in Collections:CMUL: Journal Articles

Files in This Item:
There are no files associated with this item.


Items in CMUIR are protected by copyright, with all rights reserved, unless otherwise indicated.