With the surging demand for data-intensive applications, ensuring seamless content delivery in Satellite-Terrestrial Integrated Networks (STINs) is crucial, especially for remote users. Dynamic Ad Insertion (DAI) enhances monetization and user experience, while Mobile Edge Computing (MEC) in STINs enables distributed content caching and ad insertion. However, satellite mobility and time-varying topologies cause service disruptions, while excessive or poorly placed ads risk user disengagement, impacting revenue. This paper proposes a novel framework that jointly addresses three challenges: (i) service continuity-and topology-aware content caching to adapt to STIN dynamics, (ii) Distributed DAI (D-DAI) that minimizes feeder link load and storage overhead by avoiding redundant ad-variant content storage through distributed ad stitching, and (iii) revenue-aware content distribution that explicitly models user disengagement due to ad overload to balance monetization and user satisfaction. We formulate the problem as two hierarchical Integer Linear Programming (ILP) optimizations: one content caching that aims to maximize cache hit rate and another optimizing content distribution with DAI to maximize revenue, minimize end-user costs, and enhance user experience. We develop greedy algorithms for fast initialization and a Binary Particle Swarm Optimization (BPSO)–based strategy for enhanced performance. Simulation results demonstrate that the proposed approach achieves over a 4.5% increase in revenue and reduces cache retrieval delay by more than 39% compared to the benchmark algorithms.

Satellite Edge Computing (SEC) is seen as a promising solution to content caching onboard by reducing retrieval latency and improving user experience. Deciding content type, number of copies, and where to cache it in a satellite con-stellation remains challenging, requiring careful consideration of many factors, such as satellite coverage, content popularity, and resource constraints. In this paper, we study resource-aware onboard content caching strategies in a multi-layer hierarchical satellite network that includes Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geostationary Orbit (GEO) satellites. The primary objective is to design a cache placement strategy that maximizes resource utilization ratio while determining the optimal number of content copies, all within the context of a time-varying network topology that primarly occurs due to the mobility of non-geostationary (NGSO) satellites. We model a novel proximity-based hierarchical hybrid content popularity model and formulate the problem as an Integer Linear Programming (ILP) problem to utilize the resources in a network. To solve the ILP problem, we propose two algorithms: Greedy based Content Cache Placement (G_CCP) and Simulated Annealing based Content Cache Placement (SA_CCP). Extensive simulations demonstrate that both G_CCP and SA_CCP are near-optimal and outperform the benchmark in terms of cache-hit ratio, resource utilization ratio, and cache fetching duration.

Satellite Edge Computing (SEC) is seen as a promising solution for deploying network functions in orbit to provide ubiquitous services with low latency and bandwidth. Software Defined Networks (SDN) and Network Function Virtualization (NFV) enable SEC to manage and deploy services more flexibly. In this paper, we study a dynamic and topology-aware VNF mapping and scheduling strategy within an SDN/NFV-enabled SEC infrastructure. Our focus is on meeting the stringent requirements of mission-critical (MC) applications, recognizing their significance in both satellite-to-satellite and edge-to-satellite communications while ensuring service delay margin fairness across various time-sensitive service requests. We formulate the VNF mapping and scheduling problem as an Integer Nonlinear Programming problem (INLP), with the objective ofminimaxfairness among specified requests while considering dynamic satellite network topology, traffic, and resource constraints. We then propose two algorithms for solving theINLPproblem: Fairness-Aware Greedy Algorithm for Dynamic VNF Mapping and Scheduling (FAGD_MASC) and Fairness-Aware Simulated Annealing-Based Algorithm for Dynamic VNF Mapping and Scheduling (FASD_MASC) which are suitable for low and high service arrival rates, respectively. Our extensive simulations demonstrate that bothFAGD_MASCandFASD_MASCapproaches are very close to the optimization-based solution and outperform the benchmark solution in terms of service acceptance rates.

Satellite edge computing (SEC) has emerged as a promising technology to deliver network services to remote users. Coupled with software-defined networking (SDN) and network function virtualization (NFV), SEC can provide flexibility, agility, and efficiency when allocating computing and storage resources. However, there still remain a number of technical challenges in terms of fairness and efficiency of the allocation of physical resources in service provisioning, especially in a satellite network with limited resources and dynamic traffic demands. In this paper, we investigate a dynamic virtual network function (VNF) mapping and scheduling in an SDN/NFV-enabled SEC environment to maximize the fairness between competing services in terms of the E2E delay safe margin to enhance the service acceptance rates in the network. We mathematically formulate the VNF mapping and scheduling problem as a nonlinear integer optimization problem, which is NP-hard. In order to effectively solve the problem, this paper proposes a two-stage heuristic dynamic VNF mapping and scheduling algorithm: i) the path selection algorithm returns all possible paths for a given service request with multiple VNFs, which are sorted in ascending order based on their E2E service delay and executed offline, and ii) the dynamic VNF mapping and scheduling algorithm performs online dynamic remapping and rescheduling of VNFs. Finally, numerical results are provided to demonstrate that the proposed algorithm offers a higher service acceptance rate, computing resource utilization efficiency, and higher fairness compared to a benchmark scheme.