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

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.