Video fluency prediction based on network features using deep learning

With the explosively increasing video traffic, ensuring the smooth playback of a video has been a challenging problem especially in the fifth generation (5G) mobile communication system. To improve the quality of experience (QoE) of a video playback, the real-time prediction of the video stuck can be a help. In this paper, we firstly select eight features from different layers to reflect the quality of video playback. Then, two models, long and short term memory (LSTM)-based Prediction Model and Gated recurrent unit(GRU)-based Prediction Model, are proposed to predict the stuck state of playback. Finally, to evaluate the effectiveness of the two proposed prediction models, we present the simulation results of accuracy and loss of the two models. Besides, comparison between traditional methods and the proposed one are provided with performance gain in terms of the accuracy, recall, confusion matrix as well as F1-score.