This paper proposes a novel approach for enhancing the performance of multicell MIMO communication systems by incorporating intelligent reflecting surfaces (IRSs) at the cell boundaries. The goal is to maximize the weighted sum rate (WSR) of all users by jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS, while adhering to the power and unit modulus constraints of the BSs and IRS. The proposed method addresses the non-convexity of the optimization problem by reformulating it into an equivalent form and solving it using the block coordinate descent (BCD) algorithm. The BCD algorithm alternately optimizes the precoding matrices and phase shifts, with the optimal precoding matrices derived in closed form when the phase shifts are fixed. Two efficient algorithms, the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method, are proposed for solving the phase shift optimization problem. Both algorithms are guaranteed to converge to at least locally optimal solutions. The proposed algorithms are extended to more general scenarios involving multiple IRSs and network MIMO. Simulation results demonstrate that the introduction of IRSs significantly enhances the performance of cell-edge users by improving the BS-IRS and IRS-user links. The optimal placement of IRSs at the cell boundary and near user clusters is shown to achieve the highest gains for cell-edge users. The paper also provides a detailed analysis of the computational complexity of the proposed algorithms.This paper proposes a novel approach for enhancing the performance of multicell MIMO communication systems by incorporating intelligent reflecting surfaces (IRSs) at the cell boundaries. The goal is to maximize the weighted sum rate (WSR) of all users by jointly optimizing the active precoding matrices at the base stations (BSs) and the phase shifts at the IRS, while adhering to the power and unit modulus constraints of the BSs and IRS. The proposed method addresses the non-convexity of the optimization problem by reformulating it into an equivalent form and solving it using the block coordinate descent (BCD) algorithm. The BCD algorithm alternately optimizes the precoding matrices and phase shifts, with the optimal precoding matrices derived in closed form when the phase shifts are fixed. Two efficient algorithms, the Majorization-Minimization (MM) Algorithm and the Complex Circle Manifold (CCM) Method, are proposed for solving the phase shift optimization problem. Both algorithms are guaranteed to converge to at least locally optimal solutions. The proposed algorithms are extended to more general scenarios involving multiple IRSs and network MIMO. Simulation results demonstrate that the introduction of IRSs significantly enhances the performance of cell-edge users by improving the BS-IRS and IRS-user links. The optimal placement of IRSs at the cell boundary and near user clusters is shown to achieve the highest gains for cell-edge users. The paper also provides a detailed analysis of the computational complexity of the proposed algorithms.