Adaptive Frame Rate Optimization Based on Particle Swarm and Neural Network for Industrial Video Stream

Abstract

The emergence of a large number of video data puts forward higher requirements on traditional video transmission technology. The new streaming media technology based on HTTP dynamic adaptive streaming DASH transmission protocol has become an important research direction of video services. How to overcome the unstable characteristics of wireless links in a limited bandwidth, achieve high-quality intelligent transmission of video, and obtain optimal user quality of experience (QoE), has become an urgent problem to be solved. This paper abandons the traditional streaming media adaptive transmission method, and combines neural network and particle swarm optimization algorithm to design a new intelligent transmission scheme. The particle swarm optimization algorithm obtains the optimal transmission parameters of QoE, and the model established by neural network predicts the optimal one. The system sets parameters to ensure video service quality under limited bandwidth and large network fluctuations in the wireless network.