This thesis investigates cost-effective and reliable resource reservation strategies for time-sensitive and safety-critical vehicular (TSSCV) applications, such as autonomous and remote driving. These applications require deterministic and guaranteed access to mobile edge computing (MEC) resources, which is typically achieved through individual reservations. Vehicles submit reservation requests to mobile network operator (MNOs), which allocate computation and communication resources based on these requests. Therefore, optimizing the timing and method for vehicles to place reservation requests in both single and multiple MNO scenarios is crucial, especially in dynamic vehicular environments with fluctuating network conditions and limited resources. Efficient reservation requests are essential as vehicles lack complete information about future resource availability and costs. Additionally, real-world uncertainties, such as unpredictable mobility, which influences future reservation times and costs, complicate the design of an optimal reservation strategy. Furthermore, dynamic pricing models employed by MNOs introduce another layer of complexity to the decision-making process for reservation requests, updates, and exchanges due to their impact on market conditions like resource supply and demand. To address the challenges of resource reservation in single MNO scenarios, this thesis proposes an advanced reservation strategy leveraging a batched long short-term memory (LSTM) model. This approach optimizes the timing of reservations, leading to significant cost savings for vehicles. To further minimize update costs, one-shot and multi-shot reservation update strategies are introduced, complemented by the heuristic greedy reservation updates (HGRU) algorithm. For multiple MNO environments, the thesis addresses cost-effective resource selection by comparing prices and network conditions across MNOs. An adaptive Markov decision process (MDP) framework is proposed, incorporating a deep reinforcement learning (DRL) algorithm, specifically dueling deep Q-learning. To enhance learning efficiency, a novel area-wise approach and adaptive MDP closely resembling real-world conditions are introduced. Furthermore, the temporal fusion transformer (TFT) is employed to effectively handle time-dependent data during model training. The multi-phase training approach, involving both synthetic and real-world data, enables the DRL agent to learn from historical data and adapt to real-time observations. Additionally, a multi-objective approach using a double deep Q-learning algorithm is proposed to minimize the cost of reservation updates while ensuring an optimal strategy and reliable provisioning. Finally, this thesis explores the use of blockchain smart contracts to establish a secure, efficient, and transparent resource trading system for vehicular networks. This blockchain-based architecture optimizes reservation costs and addresses trust issues by enabling decentralized, secure, and cost-effective resource trading among vehicles. By leveraging smart contracts, the system ensures transparency and immutability in transactions, fostering trust among participating vehicles. Simulation results demonstrate that the proposed resource reservation algorithms in single MNO environments outperform benchmark reservation schemes, including immediate reservation schemes, in terms of cost minimization and resource utilization efficiency, which also pose challenges for resource guarantee. In multiple MNO environments, the algorithms effectively manage uncertainties and promote competition between MNOs than single MNO scenario potentially impacting guaranteed resource provisioning. Additionally, while the exchange reservation strategies enhance security, the absence of robust security mechanisms could result in unreliable resource requests from providers, posing challenges to guaranteed resource provisioning.