The rise of intelligent transportation systems has revolutionized how vehicles interact, enabling real-time data exchange with each other and roadside infrastructure. This evolution enhances road safety, optimizes traffic flow, and improves the overall urban driving experience. However, as vehicle connectivity scales up in densely populated cities, the demand on wireless spectrum grows rapidly, exposing the limitations of conventional orthogonal multiple access (OMA) techniques. OMA's rigid one-user-per-channel allocation struggles under high load, leading to inefficient resource use and degraded Quality of Service (QoS).  Non-orthogonal multiple access (NOMA) offers a more efficient alternative by allowing multiple users to share the same channel through power-domain multiplexing and successful interference cancellation. While NOMA boosts spectral efficiency, applying it to fast-changing vehicular networks presents complex challenges including user pairing, channel assignment, power allocation, and dynamic switching between NOMA and OMA modes.

This thesis presents an adaptive optimization framework that addresses these challenges holistically by integrating RSU-vehicle user (VU) association, channel assignment, transmit power control, and NOMA-OMA switching into a unified model. The proposed solution leverages mathematical optimization techniques to balance system efficiency and real-time applicability. To manage complexity, we introduce simplification methods such as binary relaxation and problem decomposition, enabling the framework to operate within practical time constraints.  Through extensive MATLAB simulations in both static and dynamic traffic scenarios, including setups with up to 20 vehicles and 5 RSUs, the framework demonstrates strong performance. Compared to baseline approaches like random association and utility-based assignment, our method reduces total transmit power by 27% and improves fairness in user association by 18%, as measured by Jain's fairness index. The framework proves scalable, robust, and highly effective in dense urban environments where network conditions change rapidly.

Overall, this work presents a power-efficient, fair, and scalable resource allocation framework tailored for NOMA-enabled vehicular networks. It establishes a comprehensive foundation for future research in distributed resource coordination, real-time implementation in large-scale vehicular systems, and adaptive management of complex, dynamic traffic and mobility patterns.