Fine-Grained Video Traffic Classification Based on QoE Values

Abstract

The paper proposes a set of features suitable for fine-grained traffic classification of network video, with data collected from real network. These features are parameters related to quality of experience (QoE), which reflects the user’s perception. The QoE value is calculated based on the ITU-T P.1201/Amd2 standard. Under this standard, each video flow can calculate corresponding QoE value and its probability of distribution. One innovative aspect of the paper is that the characteristics of QoE value and its probability distribution are extracted as the discriminating features which are suitable for video traffic classification. The extracted features of QoE distribution are typically mean, variance, maximum and minimum statistical characteristics, and the probability distribution of features can be obtained. Different from previous work, in our method, we obtain for the first time the discrete distribution of probability with five values, and use them directly as independent features to participate in feature selection and classification. The experimental results demonstrate that the proposed new features can significantly improve classification accuracy compared with an existing method.