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The main goal of today’s wireless Service Providers (SPs) is to provide optimum and ubiquitous service for roaming users while maximizing the SPs own monetary profits. The fundamental objective is to support such requirements by providing solutions that are adaptive to varying conditions in highly mobile and heterogeneous, as well as dynamically changing wireless network infrastructures. This can only be achieved through well-designed management systems. Most techniques fail to utilize the knowledge gained from previously tested reconfiguration strategies on system and network behaviour. This dissertation presents a novel framework that automates the cooperation among a number of wireless SPs facing the challenge of meeting strict service demands for a large number of mobile users. The proposed work employs a novel policy-based system configuration model to automate the process of adapting new network policies. The proposed framework relies on the assistance of a real-time simulator that runs as a constant background process in order to continuously find optimal policy configurations for the SPs’ networks. To minimize the computational time needed to find these configurations, a modified tabu-search scheme is proposed. An objective is to efficiently explore the space of network configurations in order to find optimal network decisions and provide a service performance that adheres to contracted service level agreements. This framework also relies on a distributed Quality of Service (QoS) monitoring scheme. The proposed scheme relies on the efficient identification of candidate QoS monitoring users that can efficiently submit QoS related measurements on behalf of their neighbors. These candidate users are chosen according to their devices’ residual power and transmission capabilities and their estimated remaining service lifetime. Service monitoring users are then selected from these candidates using a novel user-to-user semantic similarity matching algorithm. This step ensures that the monitoring users are reporting on behalf of other users that are highly similar to them in terms of their mobility, used services and device profiles. Experimental results demonstrate the significant gains achieved in terms of the reduced traffic overhead and overall consumed users’ devices power while achieving a high monitoring accuracy, adaptation time speedup, base station load balancing, and individual providers’ payoffs.