# Optimal Proactive Caching for Multi-View Streaming Mobile Augmented Reality
## 2 Related Work  
This section presents the literature review which is closely relevant, whilst at the same time, we identify how the proposed algorithms approach and differ from previously proposed solutions. In [17], the caching resource sharing problem is considered separately in a Radio Access Network (RAN) and the Core Network (CN) under the view of fully 5G virtualization. Similar to our work, a probability of requesting a set of popular content is assumed, and this information is used to formulate the actual cache hit ratio. More specifically, in [17], a dynamic Cournot game in RAN is firstly solved, and this provides a probability sorting algorithm to achieve the local optima for the CN. Then, a global equilibrium algorithm is proposed to achieve the balance between the given local optimal solutions [17]. Although the demand and competition in mobile virtual network operators are carefully researched in [17], the authors in [18] point out that the chaining of virtual network functions is also a key feature of 5G and beyond virtual networks and should be considered. In [18], a set of network slices is applied to generate a centralized optimization problem that maximizes system utility. A trade-off between efficiency and fairness could be achieved through selecting the utility function for their optimization problem [18]. A trade-off between latency and analytic accuracy of uploaded frames in an edge cloud-based MAR system is provided in [3]. In [19], another trade-off targeting on workload and service latency is provided. In their later work, two trade-offs are designed for the MAR system, where instead of choosing different target functions under different conditions, a weighting parameter is brought in to directly achieve the balance between latency and preference. Such an idea is also utilized in this paper. However, it has to be noted that most of the previously proposed schemes for MAR do not explicitly take into account wireless transmission, user mobility and service decomposition jointly. There are other relevant works proposing improved frameworks and networking with the edge cloud support. In [20], a tensor-based cloud-edge computing framework is designed for cyber-physical-social systems, which can be separated to the cloud and edge plane. In their framework, the cloud manages large-scale, long-term and global data, while the edge servers react to real-time situations by processing small-scale, short-term and local data [20]. The proposed framework in this paper also tends to allocate decomposed services at edge servers while it turns to the further cloud when exceeding the caching or computing boundary. Another optimization framework in [21], alternating direction method of multipliers (ADMM), considers a general task and regards terminals, edge nodes and remote clouds as a three-tier network. Similar to our framework, this one also jointly optimizes offloading decisions and computing resource allocation to minimize the task delay [21]. It then reduces problem complexity by decomposing the large-scale problem into smaller sub-problems and finally provides near-optimal solutions subjecting to the battery capacity [21]. The deep learning framework in [22] is specialized for AR applications and considers trade-offs between accuracy and latency. However, they accept a more powerful local \"helper\" (e.g., home servers), and hence, the AR application is processed either locally or in a remote cloud. MAR with Viable Energy and Latency (MARVEL) also accepts a simplified network structure including mobile AR devices and clouds but integrates local inertial tracking, local optical flow and visual tracking into a complete system [23]. At the application layer, there are relevant research works focusing on resource allocation problems for different video view streams. In [24], multiple views of a live video in a cloud-based system are considered in a similar fashion with this work.