This thesis investigates the optimization of Intelligent Reflective Surfaces (IRSs) in wireless communication networks, focusing on reducing control overhead and enhancing network performance. A key contribution of this work is the introduction of a Kronecker-based phase shift model, which significantly reduces the overhead required for controlling the phase shifts of the IRSs. By representing the phase shifts of each IRS with fewer control signals, this approach simplifies the wiring systems in IRSs and enables faster switching between configurations. The research initially focuses on a downlink transmission system, formulating an optimization problem based on the minimum mean square error (MMSE) criterion. This results in a multi-variable non-convex problem, which is then tackled using alternating optimization (AO). The Kronecker-based method is shown to outperform traditional phase shift control in terms of efficiency and scalability. The thesis then extends this model to cell-free multiple-input multiple-output (MIMO) systems, incorporating multiple IRSs to enhance coverage, mitigate interference, and improve performance. Here, optimization frameworks for uplink and downlink communication are developed, focusing on joint optimization of IRS phase shifts and beamforming/receiver matrices at distributed base stations (BSs). The performance is evaluated using sum-rate and good-put metrics, with numerical simulations demonstrating that the proposed approach reduces control overhead while improving system performance in large-scale environments. The results highlight the potential of the Kronecker-based method for efficient IRS deployment in future wireless networks.