Cooperative path planning for UAVs using hierarchical optimal control is a critical technology for urban emergency communication networks. Existing methods typically decouple trajectory optimization from communication scheduling, which inherently leads to suboptimal performance. Attempting to solve the problem monolithically creates a large-scale Mixed-Integer Non-Linear Program that is computationally intractable for real-time deployment. Compounding this, current models often overlook the need for dynamic UAV role-switching, limiting the system’s functional flexibility and operational adaptability. We propose a hierarchical control framework that decomposes the problem into a dual-layer model predictive control architecture. Specifically, we linearize the original problem within a receding horizon control loop for long-term task and trajectory planning in the strategic layer. Guided by this, the lower trajectory layer employs non-linear model predictive control, solved via sequential convex programming, to generate real-time trajectories and perform adaptive role switching. Simulation results demonstrate that the proposed framework reduces mission makespan by 8.6% and increases the total data collected by 2.4%, validating its efficiency and robustness.