Super-Resolution-Empowered Adaptive Medical Video Streaming in Telemedicine Systems

Abstract

Due to influence of COVID-19, telemedicine is becoming more and more important. High-quality medical videos can provide a physician with a better visual experience and increase the accuracy of disease diagnosis, but this requires a dramatic increase in bandwidth compared to that required by regular videos. Existing adaptive video-streaming approaches cannot successfully provide high-resolution video-streaming services under poor or fluctuating network conditions with limited bandwidth. In this paper, we propose a super-resolution-empowered adaptive medical video streaming in telemedicine system (named SR-Telemedicine) to provide high quality of experience (QoE) videos for the physician while saving the network bandwidth. In SR-Telemedicine, very low-resolution video chunks are first transmitted from the patient to an edge computing node near the physician. Then, a video super-resolution (VSR) model is employed at the edge to reconstruct the low-resolution video chunks into high-resolution ones with an appropriate high-resolution level (such as 720p or 1080p). Furthermore, the neural network of VSR model is designed to be scalable and can be determined dynamically. Based on the time-varying computational capability of the edge computing node and the network condition, a double deep Q-Network (DDQN)-based algorithm is proposed to jointly select the optimal reconstructed high-resolution level and the scale of the VSR model. Finally, extensive experiments based on real-world traces are carried out, and the experimental results illustrate that the proposed SR-Telemedicine system can improve the QoE of medical videos by 17–79% compared to three baseline algorithms.

Real-Time Network Video Data Streaming in Digital Medicine

“…Nowadays, the use of video streaming in medicine is becoming more and more widespread, especially in the field of telemedicine. An example of such a solution can be seen in the publication of H. Han and J. Lv [3]. In their article, they describe a solution that can provide the doctor with video data at high resolution (720 p or 1080 p), which can be used to make a more accurate diagnosis than using a lower-resolution video stream (360 p or 480 p).…”

“…These values are stored and after a given number of pieces, an average delay between the client and the server is calculated. This is shown in Equation (3).…”