## 1 Introduction  
The rapid growth of connected and autonomous vehicles in next-generation networks has increased the need for Ultra-Reliable Low-Latency Communication (URLLC) to support mission-critical services such as autonomous driving, real-time traffic coordination, and collision avoidance within Fifth-Generation Vehicular Ad Hoc Networks (5G VANETs). However, the distributed nature of 5G VANET infrastructures presents significant challenges in managing compute, storage, and radio resources across geographically distributed nodes. Ensuring the efficient offloading and processing of critical vehicular data on edge application servers capable of maintaining optimal Quality of Service (QoS) for time-sensitive applications remains a particularly significant challenge. Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV) are emerging technologies offering transformative approaches for optimising QoS and resource management in 5G VANETs. Whilst SDN decouples the Radio Access Network (RAN) control and user plane functionalities, to enable a flexible and programmable framework for meeting the diverse and stringent URLLC-class QoS requirements, NFV introduces a new paradigm that allows network functions such as switching, routing, and security to be deployed and scaled on demand to support the increasing IoT and VANET demands in Next-Generation Networks (NGNs) [1]. A key capability of SDN is network slicing, which enables the deployment of Virtualised Network Functions (VNFs) as logically isolated segments over a shared infrastructure. Network slicing configurations may vary. A network slice may include isolated subsets of User Plane (UP) and Control Plane (CP) functions, with dedicated spectrum allocations and cell sites, as seen in Third Generation (3G) and Fourth Generation (4G) networks. Alternatively, slices can be applied across different layers of the protocol stack, shared among heterogeneous services while maintaining isolation [2; 3]. SDN's network slicing capabilities are standardised in the Open Networking Foundation (ONF) SDN architecture, which defines the functional layers, interfaces, and protocols that support dynamic control and virtualisation in Fifth Generation (5G) network environments. The ONF architecture [4] facilitates the efficient management and orchestration of network resources through an abstraction layer that enables a high degree of customisation and responsiveness to meet the stringent URLLC-class QoS requirements in 5G VANETs. In addition, 5G deployment frameworks, architectures, configurations, and use cases are effectively addressed in the Third-Generation Partnership Project (3 GPP) release 15 and 16 [1; 5]. Recent studies proposing QoS optimisation for connected vehicles in 5G VANETs have explored edge computing frameworks that incorporate both horizontal and vertical offloading strategies to reduce workload to edge and cloud servers [6; 7]. In horizontal offloading, vehicles offload data or computational tasks to nearby vehicles via Vehicle-to-Vehicle (V2V) technology, enabling reduced latency. However, this approach increases resource consumption for participating vehicles [6]. In contrast, vertical edge offloading, which is more commonly adopted, shifts data processing from vehicles to edge servers, which offer greater but limited computational capacity. Typically, vertical offloading introduces increased latency and potential network congestion. Therefore, the limited availability of resources and strict latency requirements at the 5G vehicular edge necessitates the application of intelligent management strategies to support vehicles involved in the processing of critical vehicular data, such as Camera, Light Detection and Ranging (LiDAR), and Radio Detection and Ranging (RADAR), and enhance safe offloading process. The combination of SDN and NFV offers a promising solution to enable predictive workload analysis, dynamic resource allocation, and load balancing across distributed communication infrastructures.