E-Content Pricing: Analysis and Simulation

There exists a huge demand for multimedia goods and services in the Internet. Currently available bandwidth speeds can support sale of downloadable content like CDs, e-books etc. as well as services like video-on-demand. The only constraints in the e-content market are availability of resources (server capacity and bandwidth) and consumer demand. In this paper, we develop an analytical framework to price on-demand content based on these constraints. We consider a system where a server handles requests for content on a First-Come-First-Served (FCFS)basis. We develop a model where customers can refuse the service based on their {\em capacity} to pay and their willingness to do so. We formulate the maximum expectation of revenue for such systems as a constrained optimization problem. Since our model is probabilistic and depends on potentially unknown parameter values for customer behavior, we develop an adaptive algorithm that learns these parameters online. We validate our framework using simulations. Our simulations indicate that our algorithm generates revenue close to the maximum expectation. Further, they also indicate that the algorithm is robust to transient customer behavior.