Recent research in Quality of Service (QoS) support in Vehicular Ad Hoc Networks (VANETs) focuses on developing adaptive communication mechanisms that ensure reliable data transmission despite high mobility and frequent topology changes. Modern approaches integrate cross-layer optimization, priority-based message scheduling, and dynamic resource allocation to maintain low latency, high throughput, and minimal packet loss for safety-critical and infotainment applications. Machine learning and software-defined networking techniques are increasingly employed to predict network conditions and adjust routing or bandwidth allocation accordingly. Furthermore, hybrid models combining cloud, fog, and edge computing are used to enhance QoS provisioning by processing data closer to vehicles, ensuring real-time responsiveness and scalability in complex vehicular environments.