Network service acquisition in a wireless environment requires the selection of a wireless access network. A key problem in wireless access network selection is studying rational strategies that consider negative network externality. In this chapter, we formulate the wireless network selection problem as a stochastic game with negative network externality and show that finding the optimal decision rule can be modeled as a multidimensional Markov decision process. A modified value-iteration algorithm is utilized to efficiently obtain the optimal decision rule with a simple threshold structure. We further investigate the mechanism design problem with incentive compatibility constraints, which force the networks to reveal truthful state information. The formulated problem is a mixed-integer programming problem that, in general, lacks an efficient solution. Exploiting the optimality of substructures, we introduce a dynamic programming algorithm that can optimally solve the problem in the two-network scenario. For the multinetwork scenario, the dynamic programming algorithm can outperform the heuristic greedy approach in polynomial-time complexity.