Compressed Sensing Based Traffic Prediction For 5G HetNet IoT Video Streaming

Abstract

Nowadays, IoT video applications are in a sharp rise, various real-time video streaming of video surveillance systems transmitted via Internet are widely investigated. The real-time video surveillance can actively monitor and detect the abnormal events in time. In 5G HetNets, we specifically develop a compressed sensing based linear predictor to predict the traffic load at the next moment. The results justify that our proposed method can forecast the traffic load and improve system performance.