The rise of vehicular edge computing (VEC) enables vehicles to offload resource-intensive tasks to roadside units (RSUs), improving efficiency and reducing latency. However, high mobility, dynamic resource availability, and heterogeneous quality-of-experience (QoE) requirements make task offloading and coordination highly challenging. In this paper, we investigate the heterogeneous task offloading problem in dynamic VEC environments by proposing a two-stage optimization framework, TOVEC. Our TOVEC decouples the spatio-temporally coupled decision space into two tractable subproblems and solves it with a two-stage design. In the first stage, we employ a TD3-based deep reinforcement learning algorithm to handle RSU-channel access decisions under dynamic network conditions. In the second stage, we formulate the interaction between vehicular users (VUs) and RSUs as a Stackelberg game, enabling joint task scheduling and dynamic pricing that balances VUs’ QoE optimization and RSUs’ revenue maximization. We theoretically prove the existence of a Stackelberg equilibrium and validate our TOVEC using real-world vehicular traces. The experimental results show that our method has improved the QoE (measured by task delay and energy cost) of VU and the benefits of RSU by 6.8%-41.3% and 2.2%-117.4%, respectively, compared with the baseline schemes.

In light of the frequent disaster events worldwide, the risk of communication network disruptions is increasingly prominent. Unmanned Aerial Vehicle (UAV) with flexibility and maneuverability is considered an effective solution for rapid service provisioning. Meanwhile, Non-Orthogonal Multiple Access (NOMA) enables simultaneous access for multiple User Equipments (UEs) on the same frequency band, enhancing spectral efficiency. This paper investigates a system that integrates NOMA with UAV-assisted Mobile Edge Computing (MEC) to provide efficient computational services. First, we construct a system model of UE task offloading and UAV deployment to reduce the overall service cost. Each UE and UAV strive to minimize their own cost. Then, the problem is modeled as the User Task Offloading and UAV Deployment Game (UTUD Game), and it is theoretically proven that at least one Nash equilibrium strategy exists for the offloading and deployment selection. Further, a decentralized algorithm based on game theory named Distributed Iterative Co-optimization for Offloading and Deployment (DICOD) algorithm is proposed to achieve this strategy. The performance of the algorithm is analyzed theoretically. Finally, we conduct experiments to analyze the upper bound of convergence time and evaluate the algorithm's performance in comparison with some other benchmarks.

Unmanned Aerial Vehicle (UAV)-assisted Low Earth Orbit (LEO) satellite edge computing (ULSE) networks can address the challenge communications issues in areas with harsh terrain and achieve global wireless coverage to provide services for mobile user devices (MUDs). This paper studies the LEO-UAV task offloading problem where MUDs compete for limited resources in the ULSE networks. We formulate the optimization problem with the goal of minimizing the cost of all MUDs while meeting resource constraint and satellite coverage time constraint. We first theoretically prove that this problem is NP-hard. We then reformulate the problem as a LEO-UAV task offloading game (LUTO-Game), and show that there is at least one Nash equilibrium solution for the LUTO-Game. We propose a joint UAV and LEO satellite task offloading (JULTO) algorithm to obtain the Nash equilibrium offloading strategy, and analyze the performance of the worst-case offloading strategy obtained by the JULTO algorithm. Finally, extensive experiments, including convergence analysis and comparison experiments, are carried out to validate the effectiveness of our JULTO algorithm.

As the computing resources and the battery capacity of mobile devices are usually limited, it is a feasible solution to offload the computation-intensive tasks generated by mobile devices to edge servers (ESs) in mobile edge computing (MEC) . In this article, we study the multi-user multi-server task offloading problem in MEC systems, where all users compete for the limited communication resources and computing resources. We formulate the offloading problem with the goal of minimizing the cost of the users and maximizing the profits of the ESs. We propose a hierarchical EETORP (Economic and Efficient Task Offloading and Resource Purchasing) framework that includes a two-stage joint optimization process. Then we prove that the problem is NP-complete. For the first stage, we formulate the offloading problem as a multi-channel access game (MCA-Game) and prove theoretically the existence of at least one Nash equilibrium strategy in MCA-Game. Next, we propose a game-based multi-channel access (GMCA) algorithm to obtain the Nash equilibrium strategy and analyze the performance guarantee of the obtained offloading strategy in the worst case. For the second stage, we model the computing resource allocation between the users and ESs by Stackelberg game theory, and reformulate the problem as a resource pricing and purchasing game (PAP-Game). We prove theoretically the property of incentive compatibility and the existence of Stackelberg equilibrium. A game-based pricing and purchasing (GPAP) algorithm is proposed. Finally, a series of both parameter analysis and comparison experiments are carried out, which validate the convergence and effectiveness of the GMCA algorithm and GPAP algorithm.

Due to the limited computing resource and battery capability at the mobile devices, the computation-intensive tasks generated by mobile devices can be offloaded to edge servers or cloud for processing. In this paper, we study the multi-user task offloading problem in an end-edge-cloud system, in which all user devices compete for the limited communication and computing resources. Particularly, we first formulate the offloading problem with the goal of maximizing the Quality of Experience (QoE) of the users subject to resource constraints. Since each user focuses on maximizing its own QoE, we reformulate the problem as a Multi-User Task Offloading Game (MUTO-Game). We then identify an important property that for any device, both the communication interference and the degree of computing resource competition can be upper bounded. Based on the property, we further theoretically prove that there exists at least one Nash Equilibrium offloading strategy in the MUTO-Game. We propose the Game-based Decentralized Task Offloading (GDTO) approach to obtain the Nash Equilibrium offloading strategy. Finally, we analyze the upper bound for the convergence time and characterize the performance guarantee of the obtained offloading strategy for the worst case. A series of experimental results are presented, in comparison with both the centralized optimal approach and the approximate approaches.