In the Internet of Vehicles (IoV), limited wireless resources pose a significant challenge in balancing communication and sensing performance, directly impacting the quality of service for vehicle users. Integrated Sensing and Communication (ISAC) systems enable vehicles to sense target vehicles using ISAC beams while maintaining communications with base station. However, efficient resource allocation within ISAC-based IoV systems remains a critical challenge. In this chapter, the joint resource allocation problem in the ISAC-enabled IoV systems is addressed by jointly optimizing resource allocation for sensing and communication to maximize the overall value of service (VoS) performance. The optimization problem falls into mixed-integer nonlinear programming (MINLP) category and is NP-hard. Consequently, the original problem is decoupled into two subproblems. Both subproblems involve finding M-to-N point association; therefore, the matching theory can be applied to solve them. The iterative loops between two subproblems are used to allow the solution of the original problem to converge. Simulation results demonstrate that the proposed method outperforms benchmark solutions in terms of achieving at least 25% higher system VoS with low vehicle density and 140% with high vehicle density, hence demonstrating performance robustness and suitability for different IoV deployments.