The advent of virtual reality and immersive communication technologies is effecting a transformation in user experiences, enabling high-quality, low-latency interactions. In order to meet these requirements, edge caching, in particular for the transmission of virtual reality content, has become an efficient strategy for the mitigation of transmission latency and the decrease of backhaul traffic loads. This paper provides an introduction to a two-stage hybrid caching framework developed to manage the typical obstacles related to the caching of 360° video. The proposed framework comprises two stages: a learning stage, which employs a Deep Q-Network to predict cache replacement actions, and a solving stage, which utilizes Integer Linear Programming to refine and optimize caching decisions. Furthermore, an L2 edge cache architecture is designed with the goal of enhancing cache utilization and further alleviating backhaul traffic. Performance evaluations illustrate that the proposed framework significantly enhances the cache hit ratio and reduces latency and backhaul usage compared to other methods.

Immersive communication is regarded as a key driver in the evolution of Virtual Reality technologies. To offer immersive experiences, 360° video edge caching has become an effective solution for minimizing latency to popular content. In order to find the optimal placement for 360° video caching, numerous conventional optimization approaches or deep learning methods have been proposed. However, conventional optimization is computationally expensive and unsuitable for online decision-making, while relying solely on deep learning methods can not guarantee the optimality and feasibility of the solutions, such as whether solutions satisfy the constraints of the original caching problem. In this paper, a dual-engine intelligent caching framework that combines operations research and deep reinforcement learning is proposed for 360° video edge caching and content prefetching. This framework introduces a shared hierarchical caching architecture and formulates a global shared caching placement problem. Adopting a cutting-edge pruning approach, the local cache replacement algorithm is proposed. This utilizes deep reinforcement learning to predict cache boundaries, which are then used to prune the search space of integer linear programming efficiently. Numerical results show that the proposed scheme provides significantly improved performance relative to alternative caching schemes.

Virtual Network Function (VNF) placement in NTNs is challenging because Non-Terrestrial Networks (NTNs), such as satellite networks, have limited resources regarding computational power and rate. However, existing solutions do not consider satellites’ resource constraints and the bandwidth constraints of links, which are essential metrics for designing VNF placement strategies in NTNs. Utilizing Network Function Virtualization (NFV) technology to deploy related network services on satellites in VNFs is a reasonable way. This paper focuses on delay-aware VNF placement in 6G NTNs to meet the ultra-low delay requirements of different applications. In addition, we also consider how to improve the resource utilization of servers to eliminate the resource bottlenecks of resource-constrained 6G NTN facilities. Then we formulate the VNF placement problem as a weighted graph-matching problem, aiming to maximize resource utilization. We propose the Linear Programming based algorithm and the Hungarian-based algorithm to solve the VNF placement problem. Evaluation results show that our proposed solutions outperform the benchmarks regarding resource utilization and execution time.