In this paper, we present a distributed resource and power allocation scheme for multiple-resource wireless cellular networks. The global optimization of multi-cell multi-link resource allocation problem is known NP-hard in the general case. We use Gibbs sampling based algorithms to perform a distributed optimization that would lead to the global optimum of the problem. The objective of this paper is to show how to use the Gibbs sampling (GS) algorithm and its variant the Metropolis-Hasting (MH) algorithm. We also propose an enhanced method of the MH algorithm, based on a priori-known target state distribution, which improves the convergence speed without increasing the complexity. Furthermore, we study different temperature cooling strategies and investigate their impact on the network optimization and convergence speed. Simulation results have also shown the effectiveness of the proposed methods.