## 1 Introduction
### Related Works  
Vehicle platooning is a salient application of ITS with its promising ability to improve road safety whilst decreasing the fuel consumption of vehicles. Platooning comprises a group of vehicles driving together and simultaneously exchanging information. An overview of distinct projects appearing with vehicle platooning is presented in [11]. The mobility of self-driving cars in the platooning concept is explored. Adaptive cruise control in autonomous vehicles provides a vehicle with the potential to follow another vehicle ahead at entailed intervals to form and maintain the platoons. A string comprising ten vehicles is controlled through several control algorithms involving feedback and feedforward control schemes being applied [12]. Besides conventional centralized-platoon formation algorithms, adopting effective and efficient distributed or decentralized algorithms for platoon formation is recommended [13]. The strategies to validate the existing platooning algorithms are featured in the research review, and a comparative analysis accounting for certain key factors is employed [14]. Although platooning of vehicles has abundant advantages, communication range boundaries are a concern when long platoons of heavy-duty vehicles are formed. The virtual leader concept is proposed, and effective leaders are elected based on VLBI metrics. Hence, L-Platooning is proposed for forming and handling long platoons seamlessly [15]. The complexity entangled in the analysis of platooning has initiated simulative analysis, which is intended to surpass the real-time test-bed constraints. In the aspect of vehicular network and vehicle dynamics, platooning of vehicles is simulated and its potential is showcased [16]. A significant aspect of platooning that enables ITS is its capability to dynamically control the velocity and stability of vehicles traveling in the same platoon. The authors recommend having the control function of platoons extremely near to the platoon; whereas, as previously discussed, CC is inapplicable for delay-critical platooning systems. Recently, researchers started preferring MEC as an enabler for platooning systems. The extensive research on computation offloading in MEC-based V2X schemes focuses on offloading computational tasks from vehicles to MEC servers. Subsequently, platooning can be expedited by Multi-access Edge Computing. Building and maintaining a platoon demands the computational offloading and vehicle coordination enabled by MEC [17]. Having demonstrated the viability of platooning in MEC schemes, integrating MEC-directing platoon control in the V2V method is investigated by exploring the Platooning-as-a-Service (PaaS) model [18]. The Cooperative Adaptive Cruise Control (CACC) utilizingV2X communications focuses on safe driving; yet, other critical drawbacks in CACC are addressed through the MEC-based CAD approach [19]. An emerging IoV scenario involves outrageous computational tasks and performing them becomes strenuous. Therefore, V2X technology is utilized in routing computation tasks and developing a balanced optimal offloading scheme [20]. Primarily, the IEEE 802.11 Distributed Coordination Function (DCF) is adopted by vehicles to communicate information to the BS associated with the edge server. Vehicles traveling in various lanes at varying speeds deliver an uncertain amount of information to the BS causing data processing complications. Thus, joint optimization is formulated for MEC-based V2I networks [21]. Correspondingly, growing traffic needs and diversified applications necessitate tremendous processing and radio resources. MEC and D2D play a key role in addressing the mentioned challenges. A multi-objective optimization problem to maximize task completions and minimize energy and cost in D2D-enabled Het-MEC scenarios is reported [22]. Multi-access Edge Computing also plays a significant role in minimizing the computational delay of edge devices in NOMA schemes.