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The rapid deployment of new applications and the interconnection of networks with increasing diversity of technologies and capacity make it more challenging to provide end-to-end quality assurance to the value-added services, such as the transmission of real-time multimedia and mission critical data. In a network with enhancements for QoS support, pricing of network services based on the level of service, usage, and congestion provides a natural and equitable incentive for multimedia applications to adapt their sending rates according to network conditions. We have developed an intelligent service architecture that integrates resource reservation, negotiation, pricing and adaptation in a flexible and scalable way. In this paper, we present a generic pricing structure that characterizes the pricing schemes widely used in the current Internet, and introduce a dynamic, congestion-sensitive pricing algorithm that can be used with the proposed service framework. We also develop the demand behavior of adaptive users based on a physically reasonable user utility function. We introduce our multimedia testbed and describe how the proposed intelligent framework can be implemented to manage a video conference system. We develop a simulation framework to compare the performance of a network supporting congestion-sensitive pricing and adaptive reservation to that of a network with a static pricing policy. We study the stability of the dynamic pricing and reservation mechanisms, and the impact of various network control parameters. The results show that the congestion-sensitive pricing system takes advantage of application adaptivity to achieve significant gains in network availability, revenue, and user-perceived benefit relative to the fixed-price policy. Congestion-based pricing is stable and effective in limiting utilization to a targeted level. Users with different demand elasticity are seen to share bandwidth fairly, with each user having a bandwidth share proportional to its relative willingness to pay for bandwidth. The results also show that even a small proportion of adaptive users may result in a significant performance benefit and better service for the entire user population-both adaptive and nonadaptive users. The performance improvement given by the congesti- on-based adaptive policy further improves as the network scales and more connections share the resources. Finally, we complement the simulation with experimental results demonstrating important features of the adaptation process.