Source Selection and Content Dissemination for Preference-Aware Traffic Offloading

Abstract

As mobile devices become more ubiquitous, the amount of cellular traffic for multimedia content grows explosively. Therefore, data dissemination through proximity-based opportunistic communications attracts the attention of service providers who are eager for solutions of traffic offloading. In this paper, we propose PrefCast, a preference-aware opportunistic content dissemination protocol that uses as few cellular bandwidth as possible to maximally satisfy user preferences for content objects. The efficiency of PrefCast depends on 1) how does the base-station select initial sources, and 2) how does each user forward objects within a limited contact duration. Since mobile users typically form communities and have heterogeneous preferences, PrefCast's base-station selects sources that efficiently produce the maximal utility to their communities. We then derive a model to predict how much utility a forwarder can contribute to future contacts. PrefCast's users can hence use such prediction to find their optimal forwarding schedule, which maximizes the utility contribution, in a distributed way. Our trace-based evaluation shows that, without explicit source selection, PrefCast produces a 15.7 and 22.6 percent higher average utility than the protocols that only consider contact frequency or preference of local contacts, respectively. Enabling source selection in PrefCast further improves the utility by 49.3 percent.