An unprecedented era of “connected vehicles” is becoming an imminent reality. This is driven by advances in vehicular communications, and the development of in-vehicle telematics systems supporting a plethora of applications. The diversity and multitude of such developments will, however, introduce excessive congestion across wireless infrastructure, compelling operators to expand their networks. An alternative to network expansions is to develop more efficient content delivery paradigms. In particular, alleviating Radio Access Network (RAN) congestion is important to operators as it postpones costly investments in radio equipment installations and new spectrum. Efficient RAN frameworks are therefore paramount to expediting this realm of vehicular connectivity. Fortunately, the predictability of human mobility patterns, particularly that of vehicles traversing road networks, offers unique opportunities to pursue proactive RAN transmission schemes. Knowing the routes vehicles are going to traverse enables the network to forecast spatio-temporal demands and predict service outages that specific users may face. This can be accomplished by coupling the mobility trajectories with network coverage maps to provide estimates of the future rates users will encounter along a trip. In this thesis, we investigate how this valuable contextual information can enable RANs to improve both service quality and operational efficiency. We develop a collection of methods that leverage mobility predictions to jointly optimize 1) long-term wireless resource allocation, 2) adaptive video streaming delivery, and 3) energy efficiency in RANs. Extensive simulation results indicate that our approaches provide significant user experience gains in addition to large energy savings. We emphasize the applicability of such predictive RAN mechanisms to video streaming delivery, as it is the predominant source of traffic in mobile networks, with projections of further growth. Although we focus on exploiting mobility information at the radio access level, our framework is a direction towards pursuing a predictive end-to-end content delivery architecture. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2014-04-30 06:15:34.31