User Preference Aware Multimedia Pricing Model using Game Theory and Prospect Theory for Wireless Communications Providing user satisfaction is a major concern for ondemand multimedia service providers and Internet carriers in Wireless Communications. Traditionally, user satisfaction was measured objectively in terms of throughput and latency. Nowadays the user satisfaction is measured using subjective metrices such as Quality of Experience (QoE). Recently, Smart Media Pricing (SMP) was conceptualized to price the QoE rather than the binary data traffic in multimedia services. In this research, we have leveraged the SMP concept to chalk up a QoEsensitive multimedia pricing framework to allot price, based on the user preference and multimedia quality achieved by the customer. We begin by defining the utility equations for the providercarrier and the customer. Then we translate the profit maximizing interplay between the parties into a twostage Stackelberg game. We model the user personal preference using Prelec weighting function which follows the postulates Prospect Theory (PT). An algorithm has been developed to implement the proposed pricing scheme and determine the Nash Equilibrium. Finally, the proposed smart pricing scheme was tested against the traditional pricing method and simulation results indicate a significant boost in the utility achieved by the mobile customers.

A GameTheoretical Pricing Mechanism for Multiuser Rate Allocation for Video over WiMAX In multiuser rate allocation in a wireless network, strategic users can bias the rate allocation by misrepresenting their bandwidth demands to a base station, leading to an unfair allocation. Gametheoretical approaches have been proposed to address the unfair allocation problems caused by the strategic users. However, existing approaches rely on a timeconsuming iterative negotiation process. Besides, they cannot completely prevent unfair allocations caused by inconsistent strategic behaviors. To address these problems, we propose a Search Based Pricing Mechanism to reduce the communication time and to capture a useru0027s strategic behavior. Our simulation results show that the proposed method significantly reduce the communication time as well as converges stably to an optimal allocation.