In this thesis, we take an information-theoretic view of the multiple-terminal wireless network. We investigate achievable rates, in the Shannon sense, and study how to achieve them through cooperative coding and routing. Our work takes an information-theoretic approach, bearing in mind the practical side of the wireless network. First, we find the best way to route data from the source to the destination if each relay must fully decode the source message. We design an algorithm which finds a set of routes, containing a rate-maximizing one, without needing to optimize the code used by the nodes. Under certain network topologies, we achieve complete routing and coding separation, i.e., the optimizations for the route and the code can be totally separated. In addition, we propose an algorithm with polynomial running time that finds an optimal route with high probability, without having to optimize the code. Second, we study the trade-off between the level of node cooperation and the achievable rates of a coding strategy. Local cooperation brings a few practical advantages like simpler code optimization, lower computational complexity, lesser buffer/memory requirements, and it does not require the whole network to be synchronized. We find that the performance of local cooperation is close to that of whole-network cooperation in the low transmit-power-to-receiver-noise-ratio regime. We also show that when each node has only a few cooperating neighbors, adding one node into the cooperation increases the transmission rate significantly. Last, we investigate achievable rates for networks where the source data might be correlated, e.g., sensor networks, through different coding strategies. We study how different coding strategies perform in different channel settings, i.e., varying node position and source correlation. For special cases, we show that some coding strategies actually approach the capacity. Overall, our work highlights the value of cooperation in multiple-terminal wireless networks.