Poster: While You Were Sleeping- Time-Shifted Prefetching of YouTube Videos to Reduce Peak-time Cellular Data Usage

Abstract

The load on wireless cellular networks is not uniformly distributed through the day, and is significantly higher during peak-times. In this context, we present a time-shifted prefetching solution that prefetches content during off-peak periods of network connectivity. We specifically focus on YouTube as it represents a significant portion of overall cellular data-usage. We present a prediction algorithm called MANTIS using a K-nearest neighbor classifier approach and show that the algorithm can reduce the traffic during peak-times by 34% for a typical user.