## 2 Related Work
Then, to derive the optimal offloading ratio, a convex optimization problem is formulated that can maximize the system-wide profit. Finally, the payoff policy for offloading services is determined to achieve the constructed non-cooperative game's equilibrium by jointly considering TaV's position and speed, nearby vehicles, the available number of ESs, availability of the requested service, etc. The work in [14] considers a joint ES selection and computation offloading optimization problem in multiservice VECNs. The problem is considered a sequential decision-making problem, because a TaV, under the coverage of multiple ES, can select the proper one by sequentially testing them one after another. At the same time, it can make sequential decisions for finding a proper offloading ratio to the ES. However, every decision taken by TaVs can alter the surrounding environment's state. The V2V communications are exploited to be aware of the surrounding environment and the potential offloading ESs, leading to better decisions in terms of network selection and offloading. ESs have limited computing resources, and multiple tasks may be offloaded to one ES, which leads to a set of contradictions in the supply and demand of computing resources. Taking into account the distance between TaV and RSU, application and communication models, and muti-TaV competition for ES resources, the work in [15] proposes a multi-TaV non-cooperative computation offloading game of a VEC scenario, in which each TaV adjusts its offloading probability to achieve the maximum utility. The authors in [16] classify MEC servers into three categories, i.e., hot MEC, neutral MEC, and nonhot MEC. They consider the sequential dependency between components that make up the task and strive to realize vehicular task offloading through cooperation among VEC servers based on the hot zone analysis. Specifically, their goal is to provide a collaborative way to minimize vehicular application latency. On the other hand, ES can participate in the computation process together with SeVs. The work in [17] provides three available offloading modes for TaVs, i.e., local computing, edge offloading, and V2V offloading while considering the channel allocation, V2V pairing, and offloading mode selection. At the same time, one TaV can only choose one offloading mode, and when multiple compute-intensive TaVs turn to the same SeV for help, they will share the computation resource equally. Except for the offloading mode involved in [17], the work in [18] considers two extra modes, i.e., TaV offloads the task to an RSU for processing and uses V2V migration to transfer the computing results, and TaV offloads the task to an RSU for processing and uses I2I migration to transfer the computing results. To fully exploit computation resources, the authors in [19] assume that TaVs can selectively offload partial tasks to the RSU with a MEC sever or SeVs by jointly considering the delay requirements of tasks, communication distance, and the computation capability of RSUs. Then, three subproblems, i.e., an offloading-matching subproblem, a channel allocation subproblem, and a task offloading subproblem, are solved by a tabu search-based matching algorithm, a graph coloring algorithm, and a variable substitution approach, respectively. Thus, the appropriate SeVs to offload the computation tasks of TaVs, channel allocation for vehicles, and computation resource allocation can be achieved. It is quite a challenge to satisfy the delay requirements of emergency computation-intensive vehicle applications. To minimize the total task processing delay, the work in [20] divides vehicles into four sets according to whether they have task offloading requirementsor provide task processing services, and it considers task processing flexibility by deciding for each vehicle to process its tasks locally, to offload the tasks to RSU via V2I connections, or to other vehicles via V2V connections.