Optimizing Mobile Prefetching by Leveraging Usage Patterns and Social Information

Real-time entertainment constitutes the majority of traffic in today´s mobile networks. The data volume is expected to increase in the near future, whereas the mobile bandwidth capacity is likely to increase significantly slower. Especially peak hour traffic often leads to overloaded mobile networks and poor user experience. This increases costs for the mobile operator, which has to adapt to the peak demand by capacity over provisioning. The new approach proposed in this paper aims to leverage the user´s context and video meta-information to unleash the potential of video prefetching. Based on observed user interactions with social networks, the videos a user consumes from social neighbours can be predicted. Moreover, the user´s daily routine even enables a prediction of the time when videos are consumed as well as the network capabilities available at that point. First results show that partial prefetching based on content categories provides a potential for efficiently offloading mobile networks. Additionally, the user experience can be improved as freezing playbacks of videos can be decreased. Initial results show a high potential for category-based prefeching.