Video frame size modeling for user-generated traffic in an enterprise-like environment

Smart mobile devices have displaced personal computers in many daily applications such as internet browsing and email. However, for content creation, users still need to use a large display, keyboard and mouse. Many initiatives are currently working on enabling I/O functionality for content creation and peripheral access, and on preserving the grab-and-go experience where the mobile device is not tethered to the docking station but merely placed in proximity of it and the traffic is carried over Wi-Fi. Maintaining the Quality of Service (QoS) and Experience (QoE) of low-latency, high fidelity video (for example the desktop view of a smart device) when transmitted over a Wi-Fi link in heavily loaded environments has been proven problematic. In this work, we propose for the first time in the relevant literature to the best of our knowledge, a highly accurate video traffic model that is capable of predicting the volume of video traffic generated by an average user's computer during a day. Our modeling techniques are tested on real user-generated screen mirroring traffic from a large shared cube space similar to an enterprise environment, and can be easily used as source traffic generators in order to facilitate the study of H.264 transmission performance over wireless networks.