Adaptive Video Transmission Control System Based on Reinforcement Learning Approach Over Heterogeneous Networks

Video may pass through various types of heterogeneous networks during the process of transmission, which has adverse impacts on the real-time video quality. Traditional methods focus on how to compress videos based on the video flow without considering the real-time network information. This paper presents an adaptive method that combines video encoding and the video transmission control system over heterogeneous networks. This method includes the following steps: first, to collect and standardize the real-time information describing the network and the video, then to assess the video quality and calculate the video coding rate based on the standardized information, and then to process the encoded compression of the video according to the calculated coding rate and transfer the compressed video. The experiments show that there is a significant improvement for the quality of real-time videos transmission without changing the existing network, particularly the core equipment. Our solution is easy to deploy and implement quickly and may help to extensively ensure video quality for normal users. Note to Practitioners-The main objective of this work is to provide an adaptive video transmission control system and methodology to improve the real-time video quality, which takes the realtime network information into the video transmission control over heterogeneous networks. Our solution is an application-layer protocol and includes three phases: 1) to collect the network and video flow status simultaneously; 2) to adjust the parameters for video quality dynamically that come from the network and video environment feedback; and 3) to optimize the video coding rate that is in accordance with the current environment conditions. Our solution is easy to deploy and implement quickly, may help to extensively ensure video quality for normal users.