The rapid advancement of autonomous driving technologies has imposed stringent requirements on low-latency and high-reliability computation, which often exceed the capabilities of onboard processors. Vehicular edge computing (VEC) provides a promising solution by offloading computation to external servers; however, terrestrial infrastructure suffers from fragmented coverage and limited scalability, particularly in highway and rural scenarios. To address these limitations, this paper considers a multi-layer aerial VEC network integrating a high-altitude platform and multiple unmanned aerial vehicles (UAVs) to jointly provide wide-area coverage and proximity services. Different from existing works that primarily focus on latency minimization under homogeneous resources, this paper explicitly models the heterogeneous leasing pricing of aerial platforms and investigates its impact on task offloading decisions. A joint task offloading and resource allocation problem is formulated to minimize the total system cost, defined as a weighted combination of latency and economic expenditure. To ensure the feasibility of UAV-assisted offloading under high mobility, a dwell-time constraint is incorporated to restrict task execution within the effective service duration. The resulting problem is formulated as a mixed-integer nonlinear programming problem, which is solved via a low-complexity iterative algorithm based on Lagrangian duality, linear relaxation, and the alternating direction method of multipliers. Simulation results demonstrate that the proposed scheme achieves significant cost reduction compared with benchmark strategies, especially under high-mobility conditions.

With the rapid advancement of Internet of Things applications, the demand for integrated sensing, communication, computing, and caching (ISC3) functions has surged. However, existing systems optimize these functions independently, leading to suboptimal resource utilization and performance bottlenecks. In this paper, we propose a multi-layer aerial ISC3 architecture where a versatile unmanned aerial vehicle (UAV) provides edge computing and caching services to ground wireless devices (WDs) alongside its radar sensing capabilities. A high-altitude platform maintains the complete service library, delivering required services to the UAV when cache misses occur. Partial data compression is employed to reduce uplink communication overhead, where WDs partially compress their offloaded task data before transmitting to the UAV. The objective is to minimize total system energy consumption by jointly optimizing time scheduling ratios, task offloading ratios, compression selection ratios, service caching decisions, and UAV trajectory, subject to task latency, sensing quality, energy budgets, and cache capacity constraints. An efficient iterative algorithm utilizing specialized optimization techniques such as Lagrangian duality and successive convex approximation is developed to solve the resulting mixed-integer nonlinear programming problem. Extensive simulations demonstrate fast convergence under diverse network configurations, with the proposed scheme consistently outperforming all baselines by 22.5%-67.0% in total energy consumption.