The exponentially increasing demand for mobile broadband communications have led to the dense deployment of cellular networks with aggressive frequency reuse patterns. The future Fifth Generation (5G) networks are expected to overcome capacity and throughput challenges by adopting a multi-tier architecture where several low-power Base Stations (BSs) are deployed within the coverage area of the macro cell. However, Inter-Cell Interference (ICI) caused by the simultaneous usage of the same spectrum in different cells, creates severe problems. ICI reduces system throughput and network capacity, and has a negative impact on cell-edge User Equipment (UE) performance. The ecological concern is also an important issue, since the carbon dioxide emissions continue to raise due to the dense deployment of BSs. Inter-Cell Interference Coordination (ICIC) techniques are required to mitigate the impact of ICI on system performance. ICIC schemes are classified into centralized, decentralized, and hybrid approaches. In the centralized approach, a central control entity manages resource and power allocation based on the information received from the different cells. It provides the optimal resource allocation at the expense of a high signaling overhead. In decentralized ICIC schemes, each cell makes its own resource allocation decisions, and the central control entity is not required. However, decentralized ICIC schemes do not guarantee the optimal resource and power allocation. The hybrid approaches are proposed as a tradeoff between the centralized and the decentralized approaches. Resource allocation between the different cells is performed by a central control entity, while UE scheduling is decentralized to the base stations. In this thesis, we address the resource and power allocation problem in multiuser Orthogonal Frequency Division Multiple Access (OFDMA) networks such as LTE/LTE-A networks and dense small cell networks. We start by overviewing the state-of-the-art schemes, and provide an exhaustive classification of the existing ICIC approaches. This qualitative classification is followed by a quantitative investigation of several interference mitigation techniques under uniform and non-uniform UE distributions, and for various network loads and radio conditions. The obtained results allow us to select the most adequate technique for each network scenario. Then, we formulate a centralized multi-cell joint resource and power allocation problem, and prove that this problem is separable into two independent convex optimization problems. The objective function of the formulated problem consists in maximizing system throughput while guaranteeing throughput fairness between UEs. ICI is taken into account, and resource and power allocation is managed accordingly in a centralized manner. Furthermore, we introduce a decentralized game-theoretical method to solve the power allocation problem without the need to exchange signaling messages between the different cells. A multi-player game is defined, where the cells are the players, and they make their own decisions independently of each other. The solution to the proposed decentralized optimization problem corresponds to a Nash Equilibrium. We also propose a decentralized heuristic power control algorithm based on the received Channel Quality Indication (CQI) feedbacks. The intuition behind this algorithm is to avoid power wastage for UEs that are close to the serving cell, and reducing ICI for UEs in the neighboring cells. An autonomous ICIC scheme that aims at satisfying throughput demands in each cell zone is also introduced. The obtained results show that this technique improves UE throughput fairness, and it reduces the percentage of unsatisfied UEs without generating additional signaling messages. Lastly, we provide a hybrid ICIC scheme as a compromise between the centralized and the decentralized approaches. For a cluster of adjacent cells, resource and power allocation decisions are made in a collaborative manner. First, the transmission power is adjusted after receiving the necessary information from the neighboring cells. Second, resource allocation between cell zones is locally modified, according to throughput demands in each zone.