VideoTrain: A Generative Adversarial Framework for Synthetic Video Traffic Generation

Abstract

Unlike the traditional Internet application such as web browsing and peer-to-peer(P2P), video streaming has been dominating the global network traffic for the past few years, raising many challenges for network providers. With the popularity of interactive videos, a.k.a 360° videos, resource requirement for video streaming has been further increased. Prior identification of these video traffic is useful for effective provisioning of network resources, yet it is difficult due to the end-to-end encryption of data. However, with the recent advances in Machine Learning (ML) methods, prior identification of these resource-demanding traffic types has become viable. Nonetheless, they require more training data, without which leads to poor performance. Collecting more training data may also pose issues related to delayed training time. To remedy this problem, in this paper, we propose a novel Generative Adversarial Network (GAN) based data generation solution to synthesise video streaming data targeting 360°/normal video classification. Taking over 600 actual video traces and generating ≈ 30000 new traces, our post-classification results show that we can achieve 5 - 15% of accuracy improvement compared to only having actual traces.