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:  III. DATA COMPUTATION OFFLOADING 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  With the emerging vehicular applications, such as real-time situational awareness and cooperative lane change, there exist huge demands for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Folo, a novel solution for latency and quality optimized task allocation in vehicular fog computing (VFC). Folo is designed to support the mobility of vehicles, including vehicles that generate tasks and the others that serve as fog nodes. Considering constraints on service latency, quality loss, and fog capacity, the process of task allocation across stationary and mobile fog nodes is formulated into a joint optimization problem. This task allocation in VFC is known as a nondeterministic polynomial-time hard problem. In this paper, we present the task allocation to fog nodes as a bi-objective minimization problem, where a tradeoff is maintained between the service latency and quality loss. Specifically, we propose an event-triggered dynamic task allocation framework using linear programming-based optimization and binary particle swarm optimization. To assess the effectiveness of Folo, we simulated the mobility of fog nodes at different times of a day based on real-world taxi traces and implemented two representative tasks, including video streaming and real-time object recognition. Simulation results show that the task allocation provided by Folo can be adjusted according to actual requirements of the service latency and quality, and achieves higher performance compared with naive and random fog node selection. To be more specific, Folo shortens the average service latency by up to 27% while reducing the quality loss by up to 56%. 
3. Abstract of Cited Paper (BIB003):  Fog computing extends the facility of cloud computing from the center to edge networks. Although fog computing has the advantages of location awareness and low latency, the rising requirements of ubiquitous connectivity and ultra-low latency challenge real-time traffic management for smart cities. As an integration of fog computing and vehicular networks, vehicular fog computing (VFC) is promising to achieve real-time and location-aware network responses. Since the concept and use case of VFC are in the initial phase, this article first constructs a three-layer VFC model to enable distributed traffic management in order to minimize the response time of citywide events collected and reported by vehicles. Furthermore, the VFC-enabled offloading scheme is formulated as an optimization problem by leveraging moving and parked vehicles as fog nodes. A real-world taxi-trajectory-based performance analysis validates our model. Finally, some research challenges and open issues toward VFC-enabled traffic management are summarized and highlighted. 
4. Abstract of Cited Paper (BIB004):  The resource limitation of multi-access edge computing (MEC) is one of the major issues in order to provide low-latency high-reliability computing services for Internet of Things (IoT) devices. Moreover, with the steep rise of task requests from IoT devices, the requirement of computation tasks needs dynamic scalability while using the potential of offloading tasks to mobile volunteer nodes (MVNs). We, therefore, propose a scalable vehicle-assisted MEC (SVMEC) paradigm, which cannot only relieve the resource limitation of MEC but also enhance the scalability of computing services for IoT devices and reduce the cost of using computing resources. In the SVMEC paradigm, a MEC provider can execute its users’ tasks by choosing one of three ways: (i) Do itself on local MEC, (ii) offload to the remote cloud, and (iii) offload to the MVNs. We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem, whose major objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources. In order to solve it, we adopt alternative optimization techniques by decomposing the original problem into two sub-problems: Resource allocation sub-problem and node selection sub-problem. Simulation results demonstrate that our proposed scheme outperforms the existing schemes in terms of the total computation overhead.

Literature Review:
The primary reason of adopting edge computing in VEC is to solve the latency issue as the edge nodes are in proximity to the requesting vehicles. Figure 4 illustrates the data computation offloading scenario for non-high dense and high dense networks. Typically, the vehicles send the data to the core cloud that results in a significant delay. Considering several constraints such as the deadline, quality loss, transmission rate, energy efficiency, and fog capacity, the process of task allocation across VENs and SENs is formulated into an optimization problem, which is known as a non-deterministic polynomial-time hard (NP-hard) problem BIB003 or mixed integer nonlinear programming (MINP)  , BIB004 , BIB005 . The latency can be calculated based on transmission time including the round trip time between the requesting vehicle and the edge node, queuing and processing times at edge node BIB001 , BIB002 ,  , BIB003 and also handover time for the inter-offloading scenario BIB002 . The existing optimisation schemes in VEC broadly emphasised several elements, such as QoS, energy, monetary, and security. Table X summarises the characteristics of the existing offloading optimisation schemes in VEC. In this survey, the schemes are broadly divided into three groups, which are single objective optimisation, joint optimisation, and multi-objective optimisation. The three groups are discussed as follows: