# Optimal Proactive Caching for Multi-View Streaming Mobile Augmented Reality
###### Abstract
Clearly, service decomposition allows a flexible assignment that fits better in a congested network with limited cache and computing resources. As shown, for example, by view stream 4 in case (b), if the target EC has limited resources, then for the selected view stream, it is recommended to cache only the most popular AROs. As a result, users request only popular view streams, such as for example view stream 4, as shown in the figure. It can be accessed with very low latency, since it can be cached in an EC at the user proximity. Hence, by exploring the popularity of the view streams and the AROs, a more efficient allocation can be implemented without affecting the overall user experience, since caching is taking  
Figure 1: An example of multi-view streaming AR application. place by considering the most popular service composition for the end users. Hereafter, the view streams under consideration are assumed to be non real time, so that they could be cached at suitable EC locations during the service period [9]. It is also worth pointing out that in multi-view streaming AR applications, it is possible to identify popular view streams and associated AROs through historical data [8; 10]. In that case, view streams and associated AROs per stream could be optimally anchored and decomposed in different EC by exploring, at the same time, the mobility pattern of the end users. In [11], a federated learning-based scheme is proposed to predict view ports based on the view history, and then, a stochastic game is formulated to optimize virtual reality video streaming through caching high or low-quality tiles of video according to given conditions. Similarly, considering latency and user preference, we aim to optimize the allocation of decomposed functions with popular view streams and embedded AROs on different target cloud servers. A Mixed Integer Linear Problem (MILP) is formulated and solved to obtain optimal solutions in this paper. However, due to the curse of dimensionality, it is not ideal for a large network with dynamic changes. Machine learning (ML)-based techniques can be deemed as an effective method for AR applications as they are able to learn from given data and provide  
Figure 2: Illustrative toy examples where in case (**a**) mobility is not considered and (**b**) where mobility of the end user is taken into account for service decomposition and the different multi view streams on proactive resource allocation of MAR functions. reliable predictions [12; 13]. A well-known ML technique, the long short-term memory (LSTM) neural network, is utilized in this paper to learn from optimal solutions. The LSTM network resists gradient vanishing and gradient exploding by keeping the important information and key features in the long term while easily forgetting the unnecessary inferences [14; 15]. In [16], LSTM is combined with a convolutional neural network to construct a supervised ML framework that improves the estimation of channel state information in 5G wireless systems. In this paper, we consider a typical Rayleigh fading channel to model the wireless link of a 5G access point, and the potential of multi-view streaming and service decomposition is explored through a nominal LSTM network. In this paper, multiple view streams with embedded AROs are considered together with user mobility and MAR service decomposition to construct a joint optimization problem. The proposed framework seeks a balance between service latency and user preference of view streams and embedded AROs. Due to the combinatorial nature of the problem, the proposed optimization problem cannot be deemed as suitable for real-time decision making. Thus, we further propose an LSTM-based scheme to learn from optimal decision making in an offline manner to provide high-quality predictions during inference in an efficient manner suitable for real-time operation.