## 1 Introduction
### Related Work  
Resource allocation in resource-limited vehicular networks with a selected set of served services is an important topic and has been well studied [10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. The optimization objective in References [10; 11; 12; 13] was the maximization of the network capacity. A resource allocation scheme with flexible node grouping was proposed in Reference [10] to utilize resources in a more efficient manner. To meet different requirements for different vehicular links, the authors of References [11; 12] maximized the ergodic capacity of V2I links while ensuring the reliability for each V2V link with a robust resource allocation. Considering the effect of mobility on the interference relationship, an iterative algorithm based on a time interval-dependent interference graph was proposed in Reference [13]. In References [14; 15], the minimization of delay was investigated. The authors of Reference [14] proposed a radio resource scheduling scheme based on stochastic learning. The scheme in Reference [15] selected the optimal receiver to establish device-to-device links and assigned the proper channels by solving a maximum weighted independent set problem. The network utility was optimized in References [16; 17; 18]. The utility function in Reference [16] considered several key factors related to video quality, including the location, velocity, intensity, and smoothness requirements. The cloud resource allocation proposed in Reference [17] was modeled as a non-cooperative cloud resource allocation game based on the Gauss-Seidel iteration method, in which the utility reflects the performance of the user including the rate and delay. In Reference [18], the access mode selection and resource allocation scheme were formulated as a user aggregate utility maximization problem, taking into consideration the transmission delay, various network topologies, and power reduction. In Reference [19], the users' secrecy rate was optimized by a joint power and subcarrier allocation based on a maximum-minimum fairness criterion. In the above mentioned studies, the resource re-allocation has not been considered. However, due to the high mobility of vehicles, the topology of vehicular networks is highly dynamic [20]. Without the latest environmental information, the resource allocation based on outdated information may not provide an optimal performance. Therefore, to maintain an outstanding performance of resource allocation, it is necessary to execute resource re-allocation frequently. Nevertheless, frequent resource re-allocation may result in a large number of the control signaling exchanges, which leads to a large overhead and degraded latency performance. Several studies have investigated the problem of reducing resource re-allocation in vehicular networks [21; 22; 23]. The resource allocation scheme proposed in Reference [21] was based on calculating the similarity between interference state maps; re-allocation was started if the difference between the maps in two consecutive time slots exceeded a given threshold. Although the proposed scheme reduced the re-allocation in a network, the guarantee of the quality of service was not considered. Another study [22] minimized the re-allocation rate in the V2V network that underlays the cellular network. The resource allocation scheme in Reference [23] maximized the average service rate of all users with the restriction of the re-allocation rate in the virtual V2V network. The original optimization problems in References [22; 23] were transformed to deterministic optimization problems using the Lyapunov optimization. However, in these two studies, it was assumed that the resources were sufficient and thus the data queues of all the services could kept stable in the network; this assumption is not applicable to future vehicular networks, in which resources are insufficient compared with the rapidly growing demand for services. Platooning has been regarded as a promising technology to improve road capacity and on-road safety in ITSs [24; 25; 26; 27].