Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> IV. CONTENT CACHING AND DELIVERY <s> With the rapid advances in vehicular technologies and social multimedia applications, VSNs have emerged and gained significant attention from both industry and academia. However, due to the low communication ability between vehicles, heavy network traffic load, and limited storage capacity, VSNs face the challenge to improve the performance of content delivery to provide a pleasant and safe driving experience. Therefore, in this article, first we present a novel framework to deliver content in VSNs with D2D communication. In the proposed framework, moving vehicles can exchange content directly with each other according to D2D communication. All contents are managed in a content-centric mode, where moving vehicles can send their interests to obtain content with naming information, resulting in a reduction of network traffic load. Based on the D2D communication, parked vehicles around the street can form vehicular social communities with the moving vehicles passing along the road, where the storage capacity of VSNs can be increased by using the contents in parked vehicles. Then we present the detailed process of content delivery in VSNs including interest sending, content distribution, and content replacement. Finally, experiment results prove the efficiency of the proposal. <s> BIB001 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> IV. CONTENT CACHING AND DELIVERY <s> Mobile devices today are constantly generating and consuming a tremendous amount of content on the Internet. Caching of such "massive" data is beyond the capacity of existing cellular networks in terms of both cost and bandwidth due to its connection-centric nature. The increasing demand for content poses fundamental questions like where, what, and how to cache and retrieve cached content. Leveraging the shift toward a content-centric networking paradigm, we propose to cache content close to the mobile user to avoid wasting resources and decrease access delays. Therefore, we present SAVING, a socially aware vehicular information-centric networking system for content storage and sharing over vehicles using their computing, caching, and communication (3Cs) capabilities. The encapsulated 3Cs are exploited first to identify the potential candidates, socially important, to cache in the fleet of vehicles. To achieve this, we propose a novel vehicle ranking system allowing a smart vehicle to autonomously "compute" its eligibility to address the question, where to cache. The identified vehicles then collaborate to efficiently "cache" content between them based on the content popularity and availability to decide what and how to cache. Finally, to facilitate efficient content distribution, we present a socially aware content distribution protocol allowing vehicles to "communicate" to address the question how to retrieve cached content. Implementation results for SAVING on 2986 vehicles with realistic mobility traces suggests it as an efficient and scalable computing, caching, and communication system. <s> BIB002 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> IV. CONTENT CACHING AND DELIVERY <s> With Vehicular mobile communication becoming an everyday requirement and an ever increasing number of services available, it is clear that vehicular networks require more efficient management. In this paper we discuss service orientation as an architectural model for Information Centric Networking (ICN) VANETs. We discuss the limitations faced by vehicles and propose structuring communication towards as a coordinated service-centric network. Intermittent connectivity and end-to-end network delay are amongst the issues tackled by this work as we envision our network model. Additionally, we perform a set of simulations to exemplify the benefits of service coordination. <s> BIB003 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> IV. CONTENT CACHING AND DELIVERY <s> One of the major goals of the 5G technology roadmap is to create disruptive innovation for the efficient use of the radio spectrum to enable rapid access to bandwidth-intensive multimedia services over wireless networks. The biggest challenge toward this goal lies in the difficulty in exploiting the multicast nature of the wireless channel in the presence of wireless users that rarely access the same content at the same time. Recently, the combined use of wireless edge caching and coded multicasting has been shown to be a promising approach to simultaneously serve multiple unicast demands via coded multicast transmissions, leading to order-of-magnitude bandwidth efficiency gains. However, a crucial open question is how these theoretically proven throughput gains translate in the context of a practical implementation that accounts for all the required coding and protocol overheads. In this article, we first provide an overview of the emerging caching- aided coded multicast technique, including state-of-the-art schemes and their theoretical performance. We then focus on the most competitive scheme proposed to date and describe a fully working prototype implementation in CorteXlab, one of the few experimental facilities where wireless multiuser communication scenarios can be evaluated in a reproducible environment. We use our prototype implementation to evaluate the experimental performance of state-of-the-art caching-aided coded multicast schemes compared to state-of-the-art uncoded schemes, with special focus on the impact of coding computation and communication overhead on the overall bandwidth efficiency performance. Our experimental results show that coding overhead does not significantly affect the promising performance gains of coded multicasting in small-scale realworld scenarios, practically validating its potential to become a key next generation 5G technology. <s> BIB004 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> IV. CONTENT CACHING AND DELIVERY <s> Some of the mobility challenges in the cities can be approached with the Internet of Things, Cloud Computing, and Fog Computing. In this work we consider the city as a system of systems and focus on the interaction of those entities with the mobility systems. A key element that allows this interaction is a vehicle on-board unit. In this work we address the interconnection, enabling technologies, interoperability, scalability, geo-distribution and time constrains aspects in the architectural design of mobility systems. For this we propose the On-Board Unit architecture and cover three different levels of technification in information and communication technologies. Based on this differentiation we are able to include Cloud and Fog Computing into our proposed designs. <s> BIB005 </s> Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey <s> IV. CONTENT CACHING AND DELIVERY <s> By analogy with Internet of things, Internet of vehicles (IoV) that 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 for realizing rapid content dissemination in device-to-device vehicle-to-vehicle (D2D-V2V)-based IoV networks. 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 obtained by Bayesian nonparametric learning based on real-world social big data, which are collected from the largest Chinese microblogging service Sina Weibo and the largest Chinese video-sharing site Youku. Then, a price-rising-based iterative matching algorithm is proposed to solve the formulated joint peer discovery, power control, and channel selection problem under various quality-of-service requirements. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm from the perspectives of weighted sum rate and matching satisfaction gains. <s> BIB006
Vehicular data can be characterised by three primary groups, namely location-centric, user-centric, and vehiclecentric  . When the vehicle drives into a new city, the driver possibly acquires some spatial information on the attraction places, road conditions, live traffic, favorite restaurants or available parking spaces i.e., location-centric. Meanwhile, infotainment services like video or games may be requested by the vehicle passengers and can be analysed in terms of the users' demographics, i.e., user-centric. Information regarding the vehicles, for instance car safety, road tax, and car service or built-in sensors also can be cached at specific storage. Relaxing the burden of cloud computing, some data, such as location-centric and user-centric, can be cached locally via RSUs, vehicles or edge servers and timely shared with other vehicles, as depicted in Figure 7 . With the advent of IoV BIB006 and VSNs BIB001 have led to the V2V caching and communications become a reality. Caching mobile contents at the edge of networks may reduce the backhaul congestion and achieve the peak traffic apart from the lower latency BIB004 . Nevertheless, the high variability in vehicular connectivity and rapid changes of the vehicular network topology pose some challenges on data safety and accuracy. In retrieving the vehicular contents, the Information-Centric Networking (ICN) BIB002 used the content name despite the IP address of the caching node and is prominently applied in the vehicular networks BIB003 , BIB005 . It brings certain benefits, such as reducing the response time and the overwhelming access on the content provider. In addition to that, the key feature of the ICN is to store the most popular contents as a priority BIB002 . Table XIV summarises the characteristics of data caching and dissemination approaches in VEC. For convenience,the data caching and dissemination approaches are classified into two, which are homogeneous cache and heterogeneous cache. Table XV lists the content caching and dissemination schemes together with their advantages and disadvantages. The following explains the details of the data cache and dissemination mechanisms.