Media download optimization through prefetching and resource allocation in mobile networks

Abstract

Mobile network operators are expected to face significant traffic increase in the upcoming years. One alternative method is to intelligently move transmissions to times of network underutilization, either on 3G/4G or by offloading to WiFi. Video content, predicted by Cisco to constitute 69% of mobile traffic, offers the greatest potential for offloading. To this end, the demonstrated app strives to relieve the mobile network in a two ways. First, long-term prefetching of promising videos based on posts from the user's Online Social Network feed is performed. The knowledge about which video is likely being requested in the near future offers the opportunity to schedule the transmission according to its probability of being watched. Second, the approach is complemented with short-term prefetching, which is used whenever a content could not be downloaded by long-term prefetching. In this case, resources are optimized so as to maximize the communication efficiency while preserving the quality of service. The demonstrated app considers the smartphone's observed cellular network history to optimize the mobile throughput. A customized video player implements both the long-term and short-term prefetching. It reduces both the load on mobile networks, decreases playback pausing events and hereby achieves a high QoE. Thus, the player addresses both the operators' and the users' needs.