###### Abstract  
Introducing partial task offloading into vehicle edge computing networks (VECNs) can ease the burden placed on the Internet of Vehicles (IoV) by emerging vehicle applications and services. In this circumstance, the task offloading ratio and the resource allocation of edge servers (ES) need to be addressed urgently. Based on this, we propose a best response-based centralized multi-TaV computation resource allocation algorithm (BR-CMCRA) by jointly considering service vehicle (SeV) selection, offloading strategy making, and computing resource allocation in a multiple task vehicle (TaV) system, and the utility function is related to the processing delay of all tasks, which ensures the TaVs's quality of services (QoS). In the scheme, SeV is first selected from three candidate SeVs (CSVs) near the corresponding TaV based on the channel gain. Then, an exact potential game (EPG) is conducted to allocate computation resources, where the computing resources can be allocated step by step to achieve the maximum benefit. After the resource allocation, the task offloading ratio can be acquired accordingly. Simulation results show that the proposed algorithm has better performance than other basic algorithms. e 2022 24 69 2022 20  
cellular network [4]. The first is the direct mode, which includes V2I communication and V2V communication by using the dedicated side-link channel in the intelligent transportation systems (ITS) 5.9 GHz spectrum. The second is the vehicle-to-network communication in the mobile licensed spectrum [5]. Vehicular edge computing (VEC) achieved through V2X can significantly control end-to-end latency, which is a dominating advantage for vehicular services. Generally, vehicular services have different QoS requirements. To satisfy these requirements, wireless resource and computation resource allocation are challenges. The authors in [6] elaborate, compare, and analyze the works in each mode (i.e., V2V, V2I, and V2X) according to the uplink data offloading for delay-sensitive data. V2V data offloading refers to transmitting data from TaVs to SeVs, which can leverage the computation resources of both TaVs and SeVs. Similarly, V2I offloading schemes can help when network connectivity is scant or lacks end-to-end connectivity. With the increasing number of vehicles and their services, V2X-based data offloading schemes are an encouraging way to address the tremendous traffic and computing burden of IoV. In addition, according to [7], data can be simultaneously offloaded to different entities, which means that a TaV can perform both V2V and V2I communication simultaneously. Much work is focused on V2X-based task offloading schemes. The computing resources of SeVs are used via V2V links in [8; 9; 10; 11; 12]. Partial offloading is adopted in [8; 9], which means the authors in [8; 9] adopt Loc + SeV mode. Namely, tasks can be executed in multiple vehicle at the same time. The work in [10; 11; 12] all adopt 0-1 offloading, which means a task can only be executed locally or in a SeV. Moreover, the tasks in studies [10; 11] can be offloaded to multiple SeVs. The difference is that [11] considers the velocity of a TaV, whereas [10] does not. The authors in [13; 14; 15; 16] leverage the computation resources of ESs via V2I links. Where [13] is in Loc + Edge mode and adopts partial offloading, refs. [14; 15; 16] adopt 0-1 offloading. In addition, considering vehicles' speed, the system in [14] is modeled in a time-discrete manner. Studies [15; 16] do not consider the mobility of vehicles; ref. [15] considers the probability of task offloading. To fully use the computing resources near a TaV, refs. [17; 18; 19] jointly consider V2V and V2I links. Studies [17; 18] adopt 0-1 offloading and tasks can only be executed in one of the three entities, i.e., Loc, SeV, and ES, whereas [19] adopts partial offloading and considers two execution modes, i.e., Loc + SeV mode and Loc + Edge mode.