In this thesis, we propose an optimization framework for designing optimal precoding matrices in full-duplex wireless multiuser multi-input-multi-output (MU-MIMO) networks. We study both the sequential training phase and the data transmission phase. In the sequential training phase, to take advantage of the full-duplex capability, the base station (BS) concurrently performs channel estimation and data transmission. In the data transmission phase, to benefit from recent progresses in all-digital self-interference cancellation, we formulate an optimization problem to obtain optimal precoding matrices based on channel state information. Simulation results show that the proposed approach could significantly improve the performance of wireless MU-MIMO networks, especially when the residual self-interference is small.