We consider pricing for multiple services offered over a single telecommunications network. Each service has quality-ofservice (QoS) requirements that are guaranteed to users. Service classes may be defined by the type of service, such as voice, video, or data, as well as the origin and destination of the connection provided to the user. We formulate the optimal pricing problem as a nonlinear integer expected revenue optimization problem. We simultaneously solve for prices and the resource allocations necessary to provide connections with guaranteed QoS. We derive optimality conditions and a solution method for this class of problems, and apply to a realistic model of a multiservice communications network.

W e propose a two-stage pricing approach that enables providers of telecommunications services to guarantee quality of service (QoS) to their customers. The scheme is intended to shift demand during congestion periods to periods of lower demand by offering price discounts as an incentive to users to delay service during the high-demand periods. The price-discount offers act as a congestion-avoidance scheme that also balances communication traffic across different time periods. To get the scheme to work, we provide methods to evaluate when to offer the discounting scheme, estimate what proportion of customers accept the discounts, and how much the price discounts should be. In addition to offering a novel pricing structure, we show the optimal solution to the problem can be computed in a sequential manner from one period to the next, greatly simplifying implementation. Furthermore, we develop the model under uncertainty to emphasize the key implementation features of simple computations that can be performed in real time using sampled information online. We use simulations to demonstrate the scheme's usefulness in regulating peak period demand.

Video on demand, Efficient bandwidth, Cournot competition, Market equilibrium,

This paper considers the provisioning of transmission line bandwidth on a private network with given traffic routing for the purpose of distribution of video-on-demand service with guaranteed end-to-end quality of service. We present an architecture for video-on-demand service delivery and model the assignment of bandwidth in the distribution network as a constrained, nonlinear optimization problem. To solve this optimization problem, we develop three new auction algorithm-based solution procedures. The optimization problem assumes that there is a functional relationship between the maximum acceptable end-to-end delay and the bandwidth requirement for the links in the distribution network. Absent reliable video traffic data models for MPEG-2 format, we sample a large number of DVD-recorded movies to form a basis for randomly generated aggregate traffic streams. The aggregate traffic streams are used in a simulation experiment to measure the maximum transmission buffer occupancy for each given traffic stream for different transmission rates. Based on this simulation experiment, we derive an empirical transmission line provisioning function that guarantees delivery of all video frames without frame loss within a maximum frame delay tolerance. To illustrate the effectiveness of the proposed solution procedure for the bandwidth assignment problem, we solve to near optimality 570 small problem instances under three demand structures and 100 large problem instances with uniformly distributed demand.

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