Each day, Humanity loses thousands of persons on roads when they were traveling to work, to study or even to distract. The financial cost of these injuries is also terrifying: Some statistics evaluate the financial cost of vehicle accidents at 160 billion Euro in Europe each year. These alarming figures have driven researchers, automotive companies and public governments to improve the safety of our transportation systems and communication technologies aiming at offering safer roads and smooth driving to human beings. In this context, Vehicular Adhoc Networks, where vehicles are able to communicate with each others and with existent road side units, emerge as a promising wireless technology able to enhance the vision of drivers and offer larger telematic horizon. VANETs promising applications are not only restricted to road safety but span from vehicle trafficoptimization like flow congestion control to commercial applications like file sharing and internet access. Safety applications require that their alert information is propagated to the concerned vehicles (located in the hazardous zone) with little delay and high reliability. For these reasons, this category of applications is considered as delay sensitive and broadcast-oriented nature. While classical blind flooding is rapid, its major drawback is its huge bandwidth utilization. In this thesis, we are interested on enhancing vehicular communications under different scenarios and optimizations: First, We focus on deriving a new solution (EBDR) to disseminate alert messages among moving vehicles while maintaining it efficient and rapid. Our proposal is based on directional antennas to broadcast messages and a route guidance algorithm to choose the best path for the packets. Findings confirmed the efficiency of our approach in terms of probability of success and end-to-end delays. Moreover, in spite of the broadcast nature of the proposed technique, all transmissions stop very soon after the arrival of a packet to its destination representing a strong feature in the conception of EBDR. Second, we propose a novel mathematical framework to evaluate the performance of EBDR analytically. Although most of the proposed techniques present in literature use experimental or simulation tools to defend their performance, we rely here on mathematical models to confirm our achieved results. Our proposed framework allows to derive meaningful performance metrics including the probability of transmission success and the required number of hops to reach thefinal destination. Third, we refine our proposed broadcast-based routing EBDR to provide more efficient broadcasting by adjusting the transmission range of each vehicle based on its distance to the destination and the local node density. This mechanism allows better minimization of interferences and bandwidth's saving. Furthermore, an analytical model is derived to calculate thetransmission area in the case of a simplified node distribution. Finally, we are interested on data collection mechanisms as they make inter-vehicle communications more efficient and reliable and minimize the bandwidth utilization. Our technique uses Q-learning to collect data among moving vehicles in VANETs. The aim behind using the learning technique is to make the collecting operation more reactive to nodes mobility and topology changes. For the simulation part, we compare it to a non-learning version to study the effect of the learning technique. Findings show that our technique far outperforms other propositions and achieves a good trade off between delay and collection ratio. In conclusion, we believe that the different contributions presented in this Thesis will improve the efficiency of inter-vehicle communications in both dissemination and data collection directions. In addition, our mathematical contributions will enrich the literature in terms of constructing suitable models to evaluate broadcasting techniques in urban zones