Take-away TV: Recharging Work Commutes with Greedy and Predictive Preloading of TV Content


Mobile data offloading can greatly decrease the load on and usage of cellular data networks by exploiting opportunistic and frequent access to Wi-Fi connectivity. Unfortunately, Wi-Fi access from mobile devices can be difficult during typical work commutes, e.g., via trains or cars on highways. In this paper, we propose a new approach: to preload the mobile device with content that a user might be interested in, and thereby avoid the need for cellular data access. We demonstrate the feasibility of this approach by developing a supervised machine learning model that learns from user preferences for different types of content, and propensity to be guided by the UI of the player, and predictively preload entire TV shows. Testing on a dataset of nearly 3.9 million sessions from all over the UK to BBC TV shows, we find that predictive preloading can save over 71% of the mobile data for an average user.

IEEE Journal on Selected Areas in Communications (J-SAC)
Dmytro Karamshuk
Dmytro Karamshuk
Former Postdoc, now Research Scientist at Facebook Core Data Science.