Combination between DASH and SVM machine learning in the field of video delivery

Abstract

With the development of network and streaming media technology, network video traffic is growing rapidly. In order to better control and manage network traffic and guarantee the quality of service of network video, it is necessary to classify network video services effectively. In traffic identification and classification, feature analysis and acquisition of better features are the key points to achieve efficient classification. Starting with the characteristics of packet size distribution, rate, IP alternation, byte number ratio between downstream and upstream, number of sub-stream fragments and average packet arrival time interval, this paper uses Support Vector Machine (SVM) to verify the classification effect of this feature, and achieves a high classification accuracy