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:  C. Multi Optimisation Approaches 
1. Abstract of Cited Paper (BIB001):  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. 
2. Abstract of Cited Paper (BIB002):  Vehicular networks have attracted much attention from both industry and academia. Due to the high-speed and complex network topology of vehicles, vehicular networks have become extremely challenging. The development of mobile communication technologies, such as SDN and FC, provides a great platform for vehicular networks. SDN separates the software plane and data plane, which enables efficient management and centralized control of vehicular networks. FC is an extension of cloud computing. By pushing significant storage, control, management, and communication mechanisms onto the network edge or user equipment, FC alleviates the pressure of the core network. Accordingly, we consider a novel SDFC-VeNET architecture in this article. Based on the SDFC-VeNET architecture, mobility management and resource allocation are discussed. Simulation results show the superiority of the presented SDFC-VeNET architecture, as well as the associated handover scheme and resource allocation mechanism. Furthermore, the existing challenges and open issues are discussed. 
3. Abstract of Cited Paper (BIB003):  Vehicular network aims at providing intelligent transportation and ubiquitous network access. Edge computing is able to reduce the consumption of core network bandwidth and serving latency by processing the generated data at the network edge, and social network is able to provide precise services by analyzing user’s personal behaviors. In this paper, we propose a new network system referred to as vehicular social edge computing (VSEC) that inherits the advantages of both edge computing and social network. VSEC is capable of improving the drivers’ quality of experience while enhancing the service providers’ quality of service. In order to further improve the performance of VSEC, the network utility is modeled and maximized by optimally managing the available network resources via two steps. First, the total processing time is minimized to achieve the optimal payment of the user to each edge device for each kind of the required resource. Second, a utility model is proposed, and the available resources are optimally allocated based on the results from the first step. The two optimization problems are solved by the Lagrangian theory, and the closed-form expressions are obtained. Numerical simulations show different capacities in different scenarios, which may provide some useful insights for VSEC design. 
4. Abstract of Cited Paper (BIB004):  Vehicular fog computing, which extends the mobile cloud paradigm, is usually composed of stable infrastructures, a large volume of vehicles, portable devices, and robust networks. As a service-providing platform, it is significant to quickly obtain the required service with the aim to correctly save the energy of the corresponding nodes and effectively improve the network survivability. However, the limited capacity of components makes such a situation more complicated. This paper aims to reduce serving time by allocating the available bandwidth to four kinds of services. A utility model is built according to the above-mentioned serving methods and is solved through a two-step approach. For the first step, all the sub-optimal solutions are provided based on a Lagrangian algorithm. For the second step, an optimal solution selection process is presented and analyzed. A numerical simulation is executed to illustrate the allocation results and the optimal utility model while optimizing the survivability. 
5. Abstract of Cited Paper (BIB005):  The emergence of computation-intensive vehicle applications poses a significant challenge to the limited computation capacity of on-board equipments. Mobile edge computing has been recognized as a ...

Literature Review:
The multi-objective optimisation (MOO) is categorised for the optimisation techniques consisting of several independent utility functions for the task offloading and computing. In general, the QoS-based optimisation is separately combined with other optimisation variables, such as energy  , BIB001 , and monetary BIB002 despite other several QoS parameters BIB006 . The existing MOO frameworks are explained as follows. 1) Several QoS metrics: The study in BIB003 formulated the resource allocation with delay optimization scheme as a Markov decision process (MDP) in SDFC-VeNET, which can be solved via an equivalent Bellman equation. The solution was simplified subject to two stages, macro policy, and micro policy, where a linear approximation method and a stochastic approximation were exploited, respectively. The macro policy and micro policy handled the complete system state, i.e., traffic density state, channel state and queue state, and resource allocation, respectively. The limitation of the work is that it relied on CSI of RRH and QSI of vehicles owing to high overhead. Another work BIB006 designed a novel multi-objective task scheduling algorithm based on a bat algorithm that optimised two objectives functions, which were to minimise the total execution time and to maximise the total successful tasks. Extensive performance evaluation demonstrated that the proposed algorithm can reduce the task execution time and achieved a high offloading ratio as well as high number of successful tasks. However, the work does not specify the type of vehicle application used. 2) QoS and Energy: The authors in  developed a metaheuristic approach called Hybrid Adaptive Particle Swamp Optimisation (PSO), which was optimised using a Genetic Algorithm (GA). In this work, every three layers computed their fitness values according to three main objectives, namely i) reduced network latency, ii) decreased energy consumption of the system, and also iii) increased availability of virtual machines. The work suffers with a serious high number of iterations for the convergence of optimal value. The authors in BIB001 investigated exciting work by considering intra-fog resource management in the local fog server (LFS) and interfog resource management in the coordinator server. In former, a convex optimization model was developed by minimising the expected total energy consumption of the fog server while satisfying the data processing rate. On the other hand, for the inter-fog resource management, the optimal traffic was derived by minimising the maximum delay time of all fog servers and migrated massive data to nearby fog server (i.e., min-max optimisation). However, the work has dealt with fog servers and not with vehicles as edge nodes. 3) QoS and Monetary: The work in BIB004 used minimal processing time delay as an initial stage to solve the optimal payment of each user for CPU, RAM, and storage space of the edge device by using the Lagrangian method. Then, the maximum utility of the VSEC was set as the second stage optimisation by using the same process, which was Lagrangian for determining the optimal resource allocation scheme. Results demonstrated that the proposed optimisation approach achieved a shorter end-to-end delay and completion time compared with existing approaches due to a low number of control messages in the network and the offloading based on the available capacity at each server. The study have been more useful if the author had considered reward for the vehicles. Other researchers in BIB002 proposed four phases in Phasing Virtual Network Embedding algorithm, which were a functiongroup topology using k-core decomposition, backbone part mapping, and edge part mapping phases based on Bloom filter, and the last phase was link mapping using one of the shortest path trees called the distributed Bellman-Ford, in the SINET-based vehicular networks. The mapping process for resource allocation was based on two key objectives, which were the maximum revenue ratio and maximum acceptance ratio. Although the proposed algorithm outperforms in terms of the revenue and cost performance, the QoS subject to the deadline is not yet discussed. The work in BIB005 investigated bandwidth allocation model for TES, IES, HRTS, and SRTS and solved via a two-step approach. The first step, all the sub-optimal solutions are provided based on a Lagrangian algorithm. For the second step, the highest utility is selected as an optimal solution. The model is assumed to reduce the serving time by tuning the available bandwidth to our services without considering any constraints.