By analogy with internet of things (IoT), internet of vehicles (IoV) which enables ubiquitous information exchange and content sharing among vehicles with little or no human intervention is a key enabler for the intelligent transportation industry. In this paper, we study how to combine both the physical and social layer information to realize rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks under various quality of service (QoS) requirements. In the physical layer, headway distance of vehicles is modeled as a Wiener process, and the connection probability of D2D-V2V links is estimated by employing the Kolmogorov equation. In the social layer, the social relationship tightness that represents content selection similarities is derived by Bayesian nonparametric learning based on real-world social big data, which are collected from Sina Weibo and Youku. Then, a price-rising based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains.