You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Computation Offloading and Content Caching Delivery in Vehicular Edge Computing: A Survey 
Chapter Title:  G. Computation Offloading and Content Delivery Issues 
1. Abstract of Cited Paper (BIB001):  Driven by both safety concerns and commercial interests, vehicular ad hoc networks (VANETs) have recently received considerable attentions. In this paper, we address popular content distribution (PCD) in VANETs, in which one large popular file is downloaded from a stationary roadside unit (RSU), by a group of on-board units (OBUs) driving through an area of interest (AoI) along a highway. Due to high speeds of vehicles and deep fadings of vehicle-to-roadside (V2R) channels, some of the vehicles may not finish downloading the entire file but only possess several pieces of it. To successfully send a full copy to each OBU, we propose a cooperative approach based on coalition formation games, in which OBUs exchange their possessed pieces by broadcasting to and receiving from their neighbors. Simulation results show that our proposed approach presents a considerable performance improvement relative to the non-cooperative approach, in which the OBUs broadcast randomly selected pieces to their neighbors as along as the spectrum is detected to be unoccupied. 
2. Abstract of Cited Paper (BIB002):  Cellular networks are one of the cornerstones of our information-driven society. However, existing cellular systems have been seriously challenged by the explosion of mobile data traffic, the emergence of machine-type communications, and the flourishing of mobile Internet services. In this article, we propose CONCERT, a converged edge infrastructure for future cellular communications and mobile computing services. The proposed architecture is constructed based on the concept of control/data (C/D) plane decoupling. The data plane includes heterogeneous physical resources such as radio interface equipment, computational resources, and software-defined switches. The control plane jointly coordinates physical resources to present them as virtual resources, over which software-defined services including communications, computing, and management can be deployed in a flexible manner. Moreover, we introduce new designs for physical resources placement and task scheduling so that CONCERT can overcome the drawbacks of the existing baseband-up centralization approach and better facilitate innovations in next-generation cellular networks. These advantages are demonstrated with application examples on radio access networks with C/D decoupled air interface, delaysensitive machine-type communications, and realtime mobile cloud gaming. We also discuss some fundamental research issues arising with the proposed architecture to illuminate future research directions. 
3. Abstract of Cited Paper (BIB003):  In the connected vehicle ecosystem, a high volume of information-rich and safety-critical data will be exchanged by roadside units and onboard transceivers to improve the driving and traveling experience. However, poor-quality wireless links and the mobility of vehicles highly challenge data delivery. The IP address-centric model of the current Internet barely works in such extremely dynamic environments and poorly matches the localized nature of the majority of vehicular communications, which typically target specific road areas (e.g., in the proximity of a hazard or a point of interest) regardless of the identity/address of a single vehicle passing by. Therefore, a paradigm shift is advocated from traditional IP-based networking toward the groundbreaking information- centric networking. In this article, we scrutinize the applicability of this paradigm in vehicular environments by reviewing its core functionalities and the related work. The analysis shows that, thanks to features like named content retrieval, innate multicast support, and in-network data caching, information-centric networking is positioned to meet the challenging demands of vehicular networks and their evolution. Interoperability with the standard architectures for vehicular applications along with synergies with emerging computing and networking paradigms are debated as future research perspectives. 
4. Abstract of Cited Paper (BIB004):  Vehicular edge computing is essential to support future emerging multimedia-rich and delay-sensitive applications in vehicular networks. However, the massive deployment of edge computing infrastructures induces new problems including energy consumption and carbon pollution. This motivates us to develop BEGIN (Big data enabled EnerGy-efficient vehIcular edge computiNg), a programmable, scalable, and flexible framework for integrating big data analytics with vehicular edge computing. In this article, we first present a comprehensive literature review. Then the overall design principle of BEGIN is described with an emphasis on computing domain and data domain convergence. In the next section, we classify big data in BEGIN into four categories and then describe their features and potential values. Four typical application scenarios in BEGIN including node deployment, resource adaptation and workload allocation, energy management, and proactive caching and pushing, are provided to illustrate how to achieve energy-efficient vehicular edge computing by using big data. A case study is presented to demonstrate the feasibility of BEGIN and the superiority of big data in energy efficiency improvement. Finally, we conclude this work and outline future research open issues. 
5. Abstract of Cited Paper (BIB005):  This paper studies the joint communication, caching and computing design problem for achieving the operational excellence and the cost efficiency of the vehicular networks. Moreover, the resource allocation policy is designed by considering the vehicle's mobility and the hard service deadline constraint. These critical challenges have often been either neglected or addressed inadequately in the existing work on the vehicular networks because of their high complexity. We develop a deep reinforcement learning with the multi-timescale framework to tackle these grand challenges in this paper. Furthermore, we propose the mobility-aware reward estimation for the large timescale model to mitigate the complexity due to the large action space. Numerical results are presented to illustrate the theoretical findings developed in the paper and to quantify the performance gains attained. 
6. Abstract of Cited Paper (BIB006):  In the era of smart cities, all vehicle systems will be connected to enhance the comfort of driving, relieve traffic congestion, and enjoy in-vehicle multimedia entertainment. The vision of all vehicles connected poses a crucial challenge for an individual vehicle system to efficiently support these applications. Network virtualization is a very promising enabling solution, by allowing multiple isolated and heterogeneous virtual networks (VNs), to satisfy the different quality of service (QoS) requirements. Smart identifier network (SINET) may provide VNs through effective resources allocation and control based on its model of three layers and two domains. In this paper, we provide resource allocation and mapping of the vehicular networks through elastic network virtualization based on SINET. The appropriate vehicles are selected and generated as a function group for special service, by which the difference of heterogeneous vehicular resources is hided. Vehicular nodes are autonomously organized that each of them can evaluate others’ resource availability in a topology-aware way with information by leveraging the learning technology and make its own decision to realize the whole mapping process through a phasing virtual network embedding (PVNE) algorithm. The results demonstrate that our proposed mechanism has better performance in long-term acceptance ratio and average revenue than existing state-of-the-art solutions. 
7. Abstract of Cited Paper (BIB007):  Technological evolutions in the automobile industry, especially the development of connected and autonomous vehicles, have granted vehicles more computing, storage, and sensing resources. The necessity of efficient utilization of these resources leads to the vision of vehicular cloud computing (VCC), which can offload the computing tasks from the edge or remote cloud to enhance the overall efficiency. In this paper, we study the problem of computation offloading through the vehicular cloud (VC), where computing missions from edge cloud can be offloaded and executed cooperatively by vehicles in VC. Specifically, computing missions are further divided into computing tasks with interdependency and executed in different vehicles in the VC to minimize the overall response time. To characterize the instability of computing resources resulting from the high vehicular mobility, a mobility model focusing on vehicular dwell time is utilized. Considering the heterogeneity of vehicular computing capabilities and the interdependency of computing tasks, we formulate an optimization problem for task scheduling, which is NP-hard. For low complexity, a modified genetic algorithm based scheduling scheme is designed where integer coding is used rather than binary coding, and relatives are defined and employed to avoid infeasible solutions. In addition, a task load based stability analysis of the VCC system is presented for the cases where some vehicles within the VC are offline. Numerical results demonstrate that the proposed scheme can significantly improve the utilization of computing resources while guaranteeing low latency and system stability.

Literature Review:
The motivation of adopting edge computing in vehicular networks is primarily to solve the latency issue as the edge nodes are in proximity to the vehicles compared to the central cloud. The problem becomes worse when the autonomous vehicle occupies with many applications, yet it has a limited storage capacity. In VEC, a decision on what edge nodes compute which task or what vehicle is critical in meeting a low latency. In addition to that, a high-density vehicular network poses significant challenges on the computation and storage resources of geo-distributed edge nodes; and thus, optimum resource allocation is essential. 1) Data computation offloading: In general, the MEC in non-vehicular networks, e.g., mobile cellular networks, served by MEC servers with several options to deploy, such as colocated with the BS BIB003 , radio network controllers  and can be farther from the UE at the core network BIB001 . On the other hand, considerable VEC offloading works assumed deploying the server at the RSU (see Table V ) besides the BS. Another primary different is various kind of vehicles with specific mobility are used as edge nodes instead of mobile devices, which owing to a fast fading channel that affects the VEC performance. In terms of the communication protocol, DSRC is mostly used for the V2I communication for the case of RSU as edge nodes. Nevertheless, other cellular networks as highlighted previously are also evaluated in the VEC works. In VEC, the requesting vehicles are surrounded by several edge nodes (i.e., SEN and VEN) for the offloading decision. Identifying a reliable edge node for consistent connectivity is a big challenge because of the vehicle speed. Therefore, the edge nodes are currently characterised and selected in terms of available workload, central processing unit (CPU) processing rate, energy consumption, radio transmission rate, offloading cost, security and reputations. Since the requesting vehicle contains tempospatial data; unlike mobile cellular networks, another concern is how to BIB005 Discrete random jumps BIB006 Stochastic geometry and Poisson BIB007 optimally execute the offloading includes types of data whether segmented or not from the vehicle. Furthermore, optimum task allocation on the computational resources of edge nodes is essential to satisfy the stringent latency. Under the assumption of vehicles with multi-communication interfaces, a decision on what radio access networks is also an important problem, but thus far limited work highlighted this. From the perspective of VEN, the underutilised ones inevitably can serve many vehicles within their boundaries, but at certain extent a high density of vehicles, particularly at the road segments or junctions, can substantially serve as edge nodes and probably has high offloading requirement. The small segments within a region or small cloudlets may reduce the offloading complexity. However, this would lead to the issues of VEC overlapping region and interference called interoffloading and intraoffloading BIB002 . 2) Content Caching and Delivery: The content caching at multiple locations near the users have been widely used in wireless networks. It is beneficial in reducing the content access delay, and at the same time increasing the content hit ratio or response time and network delivery rate. Caching popular content at small base-stations and even at the UE can be exploited to mitigate the burden of backhaul and also a high cost transmission from the macro base station. In contrast, the contents in VEC are time-varying, locationdependent and delay-constrained, such as automated driving services BIB004 . In other words, the popular contents for VEC are likely short term with regards to location. Therefore, the service content must be completely fetched within few number of VEC cloudlets or else, the quality of the service will deteriorate. It is challenging for edge nodes to optimize data transmitted through wireless networks while satisfying the content deadlines due to unbalanced traffic and different density of vehicular networks. Another issue is regarding the content placement policy to choose the optimum edge nodes for caching that leads to high cache hit ratio. Both the dynamic topology of vehicular networks and the spatial distribution of data chunks have a great impact on content dissemination speed  . The underlying V2V and V2I wireless communication is important to determine an efficient content distribution.The edge nodes may use the contextual information of the vehicles within its coverage to schedule the contents to the requesting vehicles. 3) Service Handover: The mobility is critical when the vehicle migrates from one fog cloudlet to another cloudlet while the data offloading or downloading is still ongoing. As depicted in Figure 3 , this type of case is referred to intraoffloading/intradownloading in which the RSUs are connected to the same MEC. The issue becomes substantial when the vehicle handovers to an adjacent BS, as shown in the figure of the interoffloading case. The cell dwell time (i.e., cell residence time) of vehicles BIB008 is varied and affected by several factors, such as road capacity and conditions, speed limits, traffic lights and so forth. As highlighted previously, the unbalanced traffic and various cell dwell time lead to the resource utilisation issue of fog nodes that deserves an optimal strategy. The moving vehicles also suffer from service interruptions due to the fast fading channel and also the availability of the fog nodes for computation in a particular area.