Machine-Learning Based Packet Switching Method for Providing Stable High-Quality Video Streaming in Multi-Stream Transmission

Abstract

Broadcasting gateway equipment generally uses a method of simply switching to a spare input stream when a failure occurs in a main input stream. However, when the transmission environment is unstable, problems such as reduction in the lifespan of equipment due to frequent switching and interruption, delay, and stoppage of services may occur. Therefore, applying a machine learning (ML) method, which is possible to automatically judge and classify network-related service anomaly, and switch multi-input signals without dropping or changing signals by predicting or quickly determining the time of error occurrence for smooth stream switching when there are problems such as transmission errors, is required. In this paper, we propose an intelligent packet switching method based on the ML method of classification, which is one of the supervised learning methods, that presents the risk level of abnormal multi-stream occurring in broadcasting gateway equipment based on data. Furthermore, we subdivide the risk levels obtained from classification techniques into probabilities and then derive vectorized representative values for each attribute value of the collected input data and continuously update them. The obtained reference vector value is used for switching judgment through the cosine similarity value between input data obtained when a dangerous situation occurs. In the broadcasting gateway equipment to which the proposed method is applied, it is possible to perform more stable and smarter switching than before by solving problems of reliability and broadcasting accidents of the equipment and can maintain stable video streaming as well.