Future wireless networks are expected to be highly heterogeneous and ultra dense with different types of small cells underlaid with traditional macro cells. In the presence of hundreds of different types of small cells, centralized control and manual intervention in network management will be inefficient and expensive. In this case, self-organization has been proposed as a key feature in future wireless networks. In a self-organizing network, the nodes are expected to take individual decisions on their behavior. Therefore, individual decision making in resource allocation (i.e., Distributed Resource Allocation) is of vital important. The objective of this thesis is to develop a distributed resource allocation framework for self-organizing small cell networks. Game theory is a powerful mathematical tool which can model and analyze interactive decision making problems of the agents with conflicting interests. Therefore, it is a well-appropriate tool for modeling the distributed resource allocation problem of small cell networks. In this thesis, I consider three different scenarios of distributed resource allocation in self-organizing small cell networks i.e., i). Distributed downlink power and spectrum allocation to ensure fairness for a small cell network of base stations with bounded rationality, ii). Distributed downlink power control for an ultra dense small cell network of base stations with energy constraints, iii). Distributed joint uplink-downlink power control for a small cell network of possibly deceitful nodes with full-duplexing capabilities. Specifically, I utilize evolutionary games, mean field games, and repeated games to model and analyze the three aforementioned scenarios. I also use stochastic geometry, which is a very powerful mathematical tool that can model the characteristics of the networks with random topologies, to design the payoff functions for the formulated evolutionary game and the mean field game. / October 2016