Artificial intelligence – Based video traffic policing for next generation networks

The constant increase in users’ bandwidth needs, through a large variety of multimedia applications, creates the need for highly effective network traffic control. This need is imperative in wireless networks, where the available bandwidth is limited, but is very important for wired networks as well. In this work we focus on the problem of policing video traffic from sources encoded with H.264 and H.265, given that these are the major state-of-the-art standards currently in the market. Building on work that has shown that classic traffic policing schemes can lead to unnecessarily strict policing for conforming video sources, we propose the use of Artificial Intelligence (AI) – based traffic policing schemes for video traffic. We conduct a performance evaluation of several AI – based schemes with the classic token bucket and we show that our proposed Frame Size Predictor and Policer scheme improve the performance of the classic token bucket by around 90% for conforming users, while providing only slightly worse policing results for non-conforming users.