Joint Smart Pricing and Proactive Content Caching for Mobile Services

In this work, we formulate and study the profit maximization problem for a wireless service provider (SP) that encounters time-varying, yet partially predictable, demand characteristics. The disparate demand levels throughout the course of the day yield excessive service cost in the peak hour that substantially hurts the reaped profit. With the SP's ability to track and statistically predict future requests of its users, we propose to enable proactive caching of the peak hour demand ahead during off-peak times. Thus, network traffic will be smoothed out, while end-users' activity patterns are undisturbed. In addition, the SP is able to assign personalized pricing policies that strike the best balance between enhancing the certainty about the future demand for optimal proactive caching and maximizing the revenue collected from end-users. Comparing the proposed system's performance to the baseline scenario of the existing practice of no-proactive service, we show that the SP attains profit gain that grows with number of users, at least, as the first derivative of the cost function. Moreover, end-users that receive proactive caching services make strictly positive savings. Thus, we essentially demonstrate the win-win situation to be reaped through the exploitation of the consistent users' activity.