The boosting of video traffic in recent years has brought severe challenges to Internet management as video traffic shows wide variety, including harmful video traffic. For example, game video traffic should be strictly supervised for teenagers. Therefore, effectively identifying video traffic has become a fundamental issue in network management, and one of the popular and difficult subjects in existing research. To this end, we propose to extract a large-scale feature set for video traffic identification in this paper. Then, a new distribution distance-based feature selection (DDFS) approach is proposed to obtain an effective feature subset. To test the effectiveness of the extracted feature and the DDFS, we designed a video traffic collection architecture to collect different video traffic data. We carried out a set of comparative experiments on the collected dataset, the experimental data suggest that the proposed approach can achieve an accuracy of over 90% for video scene traffic identification and 98% for cloud gaming video traffic identification. Additionally, the comparison of DDFS with four feature selection methods shows that DDFS is a practical feature selection technique for video traffic identification.

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