The increased popularity of IEEE 802.11 WLANs has led to dense deployments in urban areas. Such high density leads to sub-optimal performance unless the interfering networks learn how to optimally share the spectrum. This paper proposes a set of novel fully distributed algorithms that allow (i) multiple interfering 802.11 WLANs to select their operating frequency in a way that minimizes global interference, and (ii) clients to choose their Access Point so that the bandwidth of all interfering networks is shared optimally. The proposed algorithms rely on Gibbs' sampler and optimize global network performance based on local information. They do not require explicit coordination among the wireless devices. We establish the mathematical properties of the proposed algorithms and study their performance using analytical, event-driven simulations. Our results strongly motivate the need for self-organization strategies in wireless access networks. We discuss implementation requirements and show that significant benefits can be gained even within incremental deployments and in the presence of non-cooperating wireless clients.