<p>The UAV-assisted mobile edge computing (MEC) system is suitable for scenarios such as sudden disasters and emergency rescue operations with its mobility and low deployment cost. To address the issue of limited communication coverage of UAVs, this paper proposes a UAV-assisted computation offloading strategy and resource allocation algorithm based on Device-to-Device (D2D) communication. Under the constraints of limited computing resources and maximum tolerable delay, an optimization problem is formulated to minimize the computational tasks for all terminal users. Furthermore, a Stackelberg game model of spectrum trading between UAVs and operators is also established in this paper, and the model comprehensively considers both UAV and operator’s revenues and costs, defining their respective utility function. To find the equilibrium point of the game, the analytical solution for the number of spectrum units leased by the operator is derived through convex optimization theory, and at the same time, an improved binary search algorithm is used to dynamically solve the spectrum leasing price. Moreover, to minimize the system delay under the limited computing and spectrum resources to improve UAV utility, a heuristic-based joint optimization algorithm is designed to solve the optimal offloading strategy and the resource allocation strategy. The results show that the proposed model and algorithm can significantly increase the utility of both players in the game and substantially reduce the total system delay with good performance.</p>