Vehicular Cloud Computing (VCC) exploits resources at vehicles, such as computing, storage and internet connectivity to provide services for applications supporting different ITS (Intelligent Transportation System) services. Current Vehicular Cloud (VC) systems allow Consumer Vehicles (CVs) to discover and consume offered services by nearby mobile cloud servers (vehicles). However, to consume the required services, the CVs must first select the most suitable service provider, given that each of providers is characterized by specific features, limitations and prices. To the best of our knowledge, no work to date addresses the critical question of how to select the best provider fitting the quality of services and costs requirements of the consumer vehicles. Similarly, Provider Vehicles (PVs) should adjust the provided services' features and prices under certain conditions such as the rate of consumers' requests which makes this issue even harder. In this paper, we propose GSS-VC as a new distributed game theory-based approach to manage the service provisioning in vehicular cloud. Our approach takes into account the benefit of each player and allows the CVs to find the most suitable PV based on the probability interaction between them. Simulation results are carried out using urban mobility model and illustrate the effectiveness of the proposed approach to answer the raised questions: what is the best condition under which the CVs may request the PVs for services? and how to select the best service with respect to the CV preferences? Results from extensive simulations on up to 1, 500 vehicles show that GSS-VC is a an efficient and reliable service selection scheme while achieving high QoS.