Traffic-Dependent Pricing for Delay-Sensitive Multimedia Networks

Existing network pricing solutions mainly focus on congestion-dependent pricing schemes, while ignoring the users´ traffic state information, which we shall show in this paper can be exploited to significantly improve the service provider´s revenue. In order to derive pricing strategies that explicitly take into account the users´ traffic dynamics, we propose a systematic framework of traffic-dependent pricing by focusing on delay-sensitive multimedia networks. First, we introduce a finite-state Markov chain to capture the users´ traffic dynamics, and a service demand model that is dependent on the users´ traffic state information. Thus, we relate the users´ traffic dynamics to the service provider´s pricing policy, by means of the traffic-dependent demand model. Then, we formulate the service provider´s pricing problem into a Markov decision process, and propose a low-complexity pricing algorithm, i.e., static pricing without considering the resource constraint, which can achieve a close-to-optimal performance. Next, by considering a practical scenario in which the service provider does not know the users´ traffic dynamics a priori, we propose a learning-based algorithm that allows the service provider to identify an (locally) optimal pricing policy. Finally, we conduct simulations to quantify the proposed framework of traffic-dependent pricing.