## 1 Introduction  
The development of intelligent transport systems (ITS) is expected to project benefits such as traffic safety, lower congestion, various services offered to users while overcoming the disorganization and inefficiency of traditional systems [1]. According to the European Telecommunications Standards Institute (ETSI), ITS deploys innovative communication and relevant technologies into vehicles and infrastructures for efficient traffic management [2]. On-road interaction has different modes of communication, such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), Vehicle-to-network (V2N), and vehicle-to-pedestrian (V2P), which can altogether be referred to as vehicle-to-everything (V2X) [3, 4]. V2X communication offers diverse applications and services with different quality of service (QoS) requirements (such as reliability, end-to-end latency, data rates, and throughput) [2, 5]. V2X applications and services can be categorized as autonomous/cooperative driving, traffic safety, traffic efficiency, and infotainment. Autonomous/cooperative driving refers to the V2V communication among neighboring vehicles, which requires relatively high throughput and low latency [5]. Traffic safety involves the protection of vehicles and pedestrians, which can reduce the number and stern of inter-vehicle collisions, property damages, and road users' accidents. The traffic efficiency includes intersection timing coordination, source-to-destination route planning, and geographical location and road condition information sharing. Traffic efficiency has less stringent QoS requirements compared to autonomous/cooperative driving and traffic safety [5]. Infotainment refers to non-driving service information, which can include the location of hotels, gas stations, car rental locations, and entertainment services such as video streaming. Compared to the other V2X applications and services, infotainment requires minimum QoS requirements [5]. To enhance the quality of service (QoS) in a vehicular network, [6] introduced a method that employs a vehicle-to-vehicle (V2V) approach with cooperative communications to be combined with vehicle-to-infrastructure (V2I). They achieved closed-form expressions for significant QoS performance attributes such as packet delivery ratio (PDR), packet loss rate (PLR), average end-to-end-delay, outage probability, throughput, and energy efficiency, which were tested using different investigated transmission schemes. Additionally, [7] used device-to-device (D2D)-based V2X communication network to introduce an approach for power allocation with energy efficiency optimization (EEO), which enhanced spectral utilization, data rate, power consumption, traffic load, and EE. They succeeded to prove the algorithm and the mathematical framework of their approach using simulation results. To improve the V2X utilities such as low latency and high reliability (LLHR) demands, [8] suggested a semi-persistent scheduling (SPS) strategy deploying millimeter-wave and non-orthogonal multiple access. Through the combination of the power allocation process, beam division and user clustering process, and the vehicular user equipment-resource block (VUE-RB) matching process, their SPS strategy outperformed current schemes in terms of computational complexity, convergence, and stability. Afterward, an autonomous vehicle-to-everything (AV2X) model, which adopted a new optimization method to optimize the inter-vehicle position while communicating with AV or relaying information to everything was presented by [4] to reinforce the vehicular networks' connectivity. Their model's simulation results surpassed other models regarding PDR, PLR, average delivery latency (DL), and throughput, as they defined the best position of the relay-vehicle issue mathematically as a constrained optimization problem [4].