This paper discusses novel joint (intra-cell and inter-cell) resource allocation algorithms for self-organized interference coordination in multi-carrier multiple-input multipleoutput (MIMO) small cell networks (SCNs). The proposed algorithms enable interference coordination autonomously, over multiple degrees of freedom, such as base station transmit powers, transmit precoders, and user scheduling weights. A generic α-fair utility maximization framework is considered to analyze performance-fairness trade-off, and to quantify the gains achievable in interference-limited networks. The proposed scheme involves limited inter-base station signaling in the form of two step (power and precoder) pricing. Based on this decentralized coordination, autonomous power and precoder update decision rules are considered, leading to algorithms with different characteristics in terms of user data rates, signaling load, and convergence speed. Simulation results in a practical setting show that the proposed pricing-based self-organization can achieve up to 100% improvement in cell-edge data rates, when compared to baseline optimization strategies. Furthermore, the convergence of the proposed algorithms is also proved theoretically.Index Terms-Self-organizing networks, autonomous algorithms, interference coordination, resource allocation, multipleinput multiple-output, co-channel interference, small cell networks, network utility maximization.