Joint pricing and proactive caching for data services: Global and user-centric approaches

In this work, we investigate the profit maximization problem of a network service provider through smart pricing and proactive data services. The demand characteristics of each user are dependent on the price and willingness-to-pay values of each service. By learning these characteristics, the service provider can further improve its profit performance through a proactive service of the predictable demand so as to smooth-out its load dynamics over time, and reduce the incurred cost. We formulate the joint price and proactive download allocation problem and study its impact on the expected user payments and the service provider profit. In particular, we show that proactive downloads can only enhance the expected profit of service provider and at the same time reduce the expected payments by the user, when compared with the no-proactive-service regime. The problem is studied from two perspectives: global optimization, and game theory. From the global optimization perspective, the problem is shown to be non-convex, yet an algorithm that yields a local optimal solution with better profit than the no-proactive-download scenario is developed. From the game theoretical perspective, the problem is posed as a coordination game with the user and the service provider are players. Best response dynamics are shown to converge to a Nash Equilibrium (NE) of the game, which is the local optimal solution achieved by the developed non-convex optimization algorithm.