查看文章

Mobile video streaming accounts for a considerable percentage of network traffic. However, the fluctuations in the available bandwidth cause video stalling events which negatively affect the user’s quality of experience. The recognition of stalling events is of great importance for solving this issue. Predicting stalling events helps in predicting the user’s experience as well as aids in finding solutions to mitigate the existing issues. Despite the existence of some research that attempts to predict video stalling, no one framework proposes a hybrid approach that predicts this issue at several levels. In this paper, we propose a novel framework to classify video stalling into three levels while considering several supervised learning methods. First, logistic regression is used to detect the existence of a stalling event using a binary approach. Then, a supervised Random Forest model is trained to classify using multi-class …