MEC cache, transcoding, and transmission strategies for improved differential evolution algorithm based on deep learning

Jiangli Liu; Jun You; Pingshan Liu

doi:10.1117/12.3049517

8 November 2024 MEC cache, transcoding, and transmission strategies for improved differential evolution algorithm based on deep learning

Jiangli Liu, Jun You, Pingshan Liu

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134160J (2024) https://doi.org/10.1117/12.3049517
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Multi-access edge computing (MEC) can enhance the user experience by deploying caching infrastructure at the edge. But MEC's cache and computing resources are limited. So, selectively caching and transcoding video, determining the server to obtain video are crucial to improve video service quality and reduce resource rental costs. In this paper, a MEC cache, transcoding and transmission strategy based on improved differential evolution algorithm is proposed. The aim is to minimize resource rental costs and response delays for video content providers. In order to improve the convergence speed of multi-objective differential evolution algorithm, an adaptive parameter algorithm based on deep learning is proposed to control parameter. Through a lot of experiments, compared with the existing strategy, the strategy proposed in this paper has better performance in resource rental cost, response delay and convergence speed.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiangli Liu, Jun You, and Pingshan Liu "MEC cache, transcoding, and transmission strategies for improved differential evolution algorithm based on deep learning", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134160J (8 November 2024); https://doi.org/10.1117/12.3049517

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