Hierarchical Reinforcement Learning-Based Delay-Sensitive Video Delivery in Vehicular Networks 


Vol. 49,  No. 8, pp. 1110-1117, Aug.  2024
10.7840/kics.2024.49.7.1110


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  Abstract

This paper jointly optimizes node scheduling and the delivery of video chunks in delay-sensitive dynamic video streaming using a hierarchical reinforcement learning algorithm. Specifically, we presented an algorithm capable of adjusting node scheduling and the transmission of video chunks at two different slow and fast timescales, respectively. When nodes caching content are randomly distributed, mobile users dynamically select the node from which to receive video and control the number and quality of video chunks from the selected node, depending on the channel conditions with nearby nodes, the quality of cached content, and the user’s queue status.

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  Cite this article

[IEEE Style]

Y. Kim, T. Xiang, Y. Kim, M. Choi, "Hierarchical Reinforcement Learning-Based Delay-Sensitive Video Delivery in Vehicular Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 8, pp. 1110-1117, 2024. DOI: 10.7840/kics.2024.49.7.1110.

[ACM Style]

Yunoh Kim, Tiange Xiang, Yeongjin Kim, and Minseok Choi. 2024. Hierarchical Reinforcement Learning-Based Delay-Sensitive Video Delivery in Vehicular Networks. The Journal of Korean Institute of Communications and Information Sciences, 49, 8, (2024), 1110-1117. DOI: 10.7840/kics.2024.49.7.1110.

[KICS Style]

Yunoh Kim, Tiange Xiang, Yeongjin Kim, Minseok Choi, "Hierarchical Reinforcement Learning-Based Delay-Sensitive Video Delivery in Vehicular Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 49, no. 8, pp. 1110-1117, 8. 2024. (https://doi.org/10.7840/kics.2024.49.7.1110)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880