# Deadline-Aware Scheduling for Transmitted RSU Packets in Cooperative Vehicle-Infrastructure Systems
## 2 Related Work and Contributions  
The high mobility of vehicles in CVIS creates a time-varying network topology. Its communication has intermittent connectivity, which increases not only opportunities for data distribution and network capacity, but also data transmission delays. It is more suitable for delay-tolerant services. [PERSON] et al. [8] proposed an RSU scheduling that provides V2I-based data services through multiple RSUs. The proposed scheme connects the RSU through a wired backbone network. The scheme adopts a cooperation strategy of multiple RSUs to transmit secure and non-secure data, reducing the deadline failure rate and average responsetime. [PERSON] et al. [9] proposed a V2R data access scheme based on data integrity and importance. Based on the vehicle request's initial information, they first estimated whether it could avoid invalid data access requests. On this basis, the author proposed a multi-request priority scheduling scheme considering the importance of requests and deadlines. Finally, the author used a priority-based strategy to satisfy these requests. [PERSON] et al. [10] proposed studying the problem of efficient content broadcasting through vehicle networks. They expressed it as a joint optimization problem of data prefetching and broadcast transmission scheduling with buffer size constraints and transmission rate adaptive constraints. This article considers a highway scenario covered by multiple RSUs and the deadline requirements of interactive applications. The author used a heuristic algorithm to solve the integer linear programming problem. The block must be delivered within the playback period to achieve the desired video quality during playback. [PERSON] et al. [11] proposed a network utility maximization (NUM) problem where the network utility function is a concave, component-by-component, non-decreasing function of the video quality index requested by time-averaged users. Maximization depends on the stability of all queues in the system. Due to the lack of an average delay performance guarantee in existing research, [PERSON] et al. [12] focused on the key goal of delivering next-generation real-time services before the corresponding deadline of each packet while minimizing the overall cloud network resource cost, introducing a novel queueing system, which can track the life cycle of data packets, and formalizing the optimal cloud network control problem with strict deadline constraints. Having explained the main challenge of sending packets to their destination before their life cycle expires, the author developed an equivalent formula. The relaxed flow conservation allowed the use of Lyapunov optimization to derive a provable, near-optimal, and fully distributed algorithm for the original problem. [PERSON] et al. [13] proposed an auction bidding scheme to determine the RSU in response to the computing request. In other words, auctioning the computing request to obtain bids from the least energy consuming RSUs. The scheme works in a decentralized model, which effectively reduces the complexity of its implementation. In order to process computing requests simulated as DAG applications, the DAG is divided into individual tasks using upper-order values. They also proposed a deadline-aware queue jump algorithm assigned to server queues in specific RSUs. When tasks require different delay deadlines, computing resource allocation becomes more challenging. [PERSON] et al. [14] proposed a scheduling strategy to maximize the number of tasks completed within their deadlines and stabilize the network. The Lyapunov drift plus penalty function on the queue length was used to schedule tasks. [PERSON] et al. [15] proposed a scheduling architecture that minimizes RSUs' energy consumption and achieves uniform energy consumption between adjacent RSUs. In turn, this scheme increases the request fulfillment percentage for RSUs. The proposed architecture classifies incoming requests as traditional (with less computation) or smart requests (with more computation).