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.