In response to the great progress of communications and computer technologies, aggressive deployment of broadband fiber optical network, advance of Internet technology, and the global standardization of IP technology, the telecommunication industry is moving toward a converged network, which uses a single global IP based packet-switching network to carry all types of network services. Diverse types of services demand diverse QoS requirements making it a great challenge to support potential services with guaranteed QoS on All-IP networks. Our research group proposes a Budget-Based QoS (BBQ) management architecture to facilitate network operators of diversified networks. With BBQ management architecture, network operators can adjust their network architectures and management polities to support as many services as possible with end-to-end QoS guarantee. In this thesis, based on BBQ QoS architecture, We propose an End-to-End QoS solution in BBQ management system. By bearer services concepts, a End-to-End service is decomposed into Backbone and Stub Network bearer services. Each subnetwork will carry the responsible bearer service with committed QoS quarantee. Based on the proposed hierarchical management infrastructure, we design a methodology to allocate resource and path planning in a progressive manner. This methodology will give network operators sufficient time to configure and deploy networks according to long time demand forecast. Furthermore, it allows admission controller to efficiently acquire resource in real time in admitting a service with end-to-end QoS guarantee. Because each Core Network plans its own internal path independently, End-to-End path planning is only to choose Core Networks to pass through. This scheme could reduce the enormous computation overhead such that it is adaquate for real time path setup. One critical challenge is to choose the best End-to-End paths with QoS quarantee for incoming service requests in terms of optimal resource utilization. We have developed a mathematical programming method to solve the problem. The simulation evaluation show that our optimizaiton ............