Real-time Video Transmission Optimization Based on Edge Computing in IIoT

Abstract

In the Industrial Internet of Things (IIoT) scenario, the increase of surveillance equipment brings challenges to the transmission of real-time video. It needs more efficient approaches to finish video transmission with more stability and accuracy. Therefore, we propose a self-adaptive transmission scheme of videos for multi-capture terminals under IIoT in this paper. To fit for the constant variation of network environment, we compress the videos that wait for transmitting from multi-capture terminals by reducing the non-key frames with Graph Convolutional Network (GCN). Moreover, a self-adaptive strategy of transmission is implemented on the Mobile Edge Computing (MEC) server to adjust the transmission volume of processed videos, and a multi-objective optimization algorithm is utilized to optimize the strategy of transmission during the video transmission. The relative experiments are conducted to validate the performance of the proposed scheme.