Buffer evaluation model and scheduling strategy for video streaming services in 5G-powered drone using machine learning

Abstract With regard to video streaming services under wireless networks, how to improve the quality of experience (QoE) has always been a challenging task. Especially after the arrival of the 5G era, more attention has been paid to analyze the experience quality of video streaming in more complex network scenarios (such as 5G-powered drone video transmission). Insufficient buffer in the video stream transmission process will cause the playback to freeze [1]. In order to cope with this defect, this paper proposes a buffer starvation evaluation model based on deep learning and a video stream scheduling model based on reinforcement learning. This approach uses the method of machine learning to extract the correlation between the buffer starvation probability distribution and the traffic load, thereby obtaining the explicit evaluation results of buffer starvation events and a series of resource allocation strategies that optimize long-term QoE. In order to deal with the noise problem caused by the random environment, the model introduces an internal reward mechanism in the scheduling process, so that the agent can fully explore the environment. Experiments have proved that our framework can effectively evaluate and improve the video service quality of 5G-powered UAV