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

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.