Three-dimensional scene reconstruction from sparse-view satellite images is a long-standing and challenging task. While 3D Gaussian Splatting (3DGS) and its variants have recently attracted attention for its high efficiency, existing methods remain unsuitable for satellite images due to incompatibility with rational polynomial coefficient (RPC) models and limited generalization capability. Recent advances in generalizable 3DGS approaches show potential, but they perform poorly on multi-temporal sparse satellite images due to limited geometric constraints, transient objects, and radiometric inconsistencies. To address these limitations, we propose SkySplat, a novel self-supervised framework that integrates the RPC model into the generalizable 3DGS pipeline, enabling more effective use of sparse geometric cues for improved reconstruction. SkySplat relies only on RGB images and radiometric-robust relative height supervision, thereby eliminating the need for ground-truth height maps. Key components include a Cross-Self Consistency Module (CSCM), which mitigates transient object interference via consistency-based masking, and a multi-view consistency aggregation strategy that refines reconstruction results. Compared to per-scene optimization methods, SkySplat achieves an 86 times speedup over EOGS with higher accuracy. It also outperforms generalizable 3DGS baselines, reducing MAE from 13.18 m to 1.80 m on the DFC19 dataset significantly, and demonstrates strong cross-dataset generalization on the MVS3D benchmark.

In this paper, we propose a novel mobility-aware resource provisioning scheme for edge-assisted extended reality (XR) services. The goal is to minimize resource consumption while satisfying user quality of experience (QoE) requirement, which is measured by the weighted sum of visual quality, quality variation, and round-trip interaction latency. Specifically, we present a mobility model to capture both user spatial movements and XR content interaction features. Since user viewing distance and interaction time are key model parameters that affect the spatiotemporal service demand for XR content rendering and delivery at the edge, we estimate user-specific model parameters and adopt a sample average approximation method to model the relationship between user QoE and the consumption of both communication and edge computing resources. We design a coordinate descent algorithm to make resource provisioning decisions, where a deep neural network provides a valuable initial point to accelerate convergence. Simulation results demonstrate that our proposed scheme is more efficient to utilize network resources in comparison with benchmark schemes while satisfying user QoE requirements.

In this paper, we propose a digital agent (DA)-assisted resource management scheme for enhanced user quality of experience (QoE) in integrated sensing and communication (ISAC) networks. Particularly, user QoE is a comprehensive metric that integrates quality of service (QoS), user behavioral dynamics, and environmental complexity. The novel DA module includes a user status prediction model, a QoS factor selection model, and a QoE fitting model, which analyzes historical user status data to construct and update user-specific QoE models. Users are clustered into different groups based on their QoE models. A Cramér-Rao bound (CRB) model is utilized to quantify the impact of allocated communication resources on sensing accuracy. A joint optimization problem of communication and computing resource management is formulated to maximize long-term user QoE while satisfying CRB and resource constraints. A two-layer data-model-driven algorithm is developed to solve the formulated problem, where the top layer utilizes an advanced deep reinforcement learning algorithm to make group-level decisions, and the bottom layer uses convex optimization techniques to make user-level decisions. Simulation results based on a real-world dataset demonstrate that the proposed DA-assisted resource management scheme outperforms benchmark schemes in terms of user QoE.

Semi-dense feature matching methods have shown strong performance in challenging scenarios. However, the existing pipeline relies on a global search across the entire feature map to establish coarse matches, limiting further improvements in accuracy and efficiency. Motivated by this limitation, we propose a novel pipeline, CasP, which leverages cascaded correspondence priors for guidance. Specifically, the matching stage is decomposed into two progressive phases, bridged by a region-based selective cross-attention mechanism designed to enhance feature discriminability. In the second phase, one-to-one matches are determined by restricting the search range to the one-to-many prior areas identified in the first phase. Additionally, this pipeline benefits from incorporating high-level features, which helps reduce the computational costs of low-level feature extraction. The acceleration gains of CasP increase with higher resolution, and our lite model achieves a speedup of $\sim2.2\times$ at a resolution of 1152 compared to the most efficient method, ELoFTR. Furthermore, extensive experiments demonstrate its superiority in geometric estimation, particularly with impressive cross-domain generalization. These advantages highlight its potential for latency-sensitive and high-robustness applications, such as SLAM and UAV systems. Code is available at https://github.com/pq-chen/CasP.

This paper presents a novel model-assisted learning scheme for resource allocation in environment-aware mobile augmented reality (AR) content delivery. The goal is to minimize the long-term communication resource consumption for delivering virtual content visible to an individual AR user by optimizing the communication resource allocation for user positioning and environment mapping. In specific, we first develop a mathematical model to estimate the content visibility uncertainty and the content delivery resource consumption. We then generate a reference resource allocation decision that guides a deep reinforcement learning-based decision process to efficiently adapt to non-stationary user and environment dynamics. We conduct trace-driven simulations to evaluate the performance of the proposed scheme, and the results demonstrate that, the proposed scheme significantly reduces communication resource consumption for delivering virtual content visible to an individual AR user, compared to benchmark schemes.

In this article, we propose a novel volumetric video caching and rendering approach for an edge-assisted extended reality (XR) system to enhance user Quality of Experience (QoE). Particularly, user QoE consists of visual quality and quality variation. Different quality of volumetric videos are required to be cached, rendered, and delivered to XR devices for different viewing distances within a time latency. Given the limited caching, computing, and communication resources on the edge server, we formulate a long-term user QoE maximization problem to jointly optimize video caching and rendering by considering user locations and viewing distances. To solve this problem, we first design an online optimization algorithm in which caching decisions are obtained using a regularization technique. We then develop a low-complexity binary search algorithm to determine optimal rendering quality. Extensive simulations are conducted to demonstrate that our proposed approach outperforms benchmark schemes by an average 46% improvement in terms of long-term user QoE.

Timely and energy-efficient task offloading and computing can be challenging in mobile edge computing (MEC) networks when the communication links between devices and edge servers are unreliable. In this paper, we apply multiple aerial intelligent reflective surfaces (AIRSs) to assist devices in offloading computing tasks to the edge server in a timely and reliable manner in the MEC network with poor offloading environments. To evaluate the timeliness of offloading and computing, we derive the evolution process of age-of-information (AoI) under the random arrival of the computing tasks. The association between devices and AIRSs, offloading order of computing tasks, design of IRS phase shift, and allocation of communication and computing resources are jointly optimized to minimize the average AoI and system energy consumption given computing requirements. To solve the formulated minimization problem, we propose an efficient problem-solving framework to cope with the challenge of variable coupling. Firstly, we derive a closed-form optimal IRS phase shift to provide a reliable offloading environment. Then, we optimize the association between devices and AIRSs while reducing the offloading complexity and balancing the number of devices associated with each AIRS. Finally, we develop a low-complexity task offloading and resource allocation algorithm based on convex optimization to attain a good enough solution. Simulation results indicate the proposed solution outperforms benchmarks in timeliness and energy saving.

Multicast short video streaming can enhance bandwidth utilization by enabling simultaneous video transmission to multiple users over shared wireless channels. The existing network management schemes mainly rely on the sequential buffering principle and general quality of experience (QoE) model, which may deteriorate QoE when users’ swipe behaviors exhibit distinct spatiotemporal variation. In this paper, we propose a digital twin (DT)-based network management scheme to enhance QoE. Firstly, user status emulated by the DT is utilized to estimate the transmission capabilities and watching probability distributions of sub-multicast groups (SMGs) for an adaptive segment buffering. The SMGs’ buffers are aligned to the unique virtual buffers managed by the DT for a fine-grained buffer update. Then, a multicast QoE model consisting of rebuffering time, video quality, and quality variation is developed, by considering the mutual influence of segment buffering among SMGs. Finally, a joint optimization problem of segment version selection and slot division is formulated to maximize QoE. To efficiently solve the problem, a data-model-driven algorithm is proposed by integrating a convex optimization method and a deep reinforcement learning algorithm. Simulation results based on the real-world dataset demonstrate that the proposed DT-based network management scheme outperforms benchmark schemes in terms of QoE improvement.

In this paper, we investigate joint caching and computing resource reservation for supporting location-aware augmented reality (AR) applications in an edge-assisted two-tier radio access network. We aim at minimizing the caching and computing resource consumption while satisfying the AR service delay requirement. Specifically, to capture the spatio-temporal AR service dynamics, the resource consumption minimization problem is formulated as a long-term stochastic optimization problem. Due to the time-varying service demands and tightly coupled multi-resource reservation decisions, we propose a novel resource reservation algorithm based on the Lyapunov optimization technique to solve the problem. We first transform the original long-term problem into multiple one-shot optimization problems, each of which is then solved by our designed iterative algorithm in an online manner. Simulation results demonstrate that the proposed algorithm can significantly reduce the overall resource consumption compared to benchmark algorithms.

In most of the existing works about mobile crowd sensing, the service provider collects data from each mobile user separately. However, comparing with the collection of data from individual users, batch trading is more attractive for both service provider and mobile users. On one hand, the service provider prefers to buy a batch of data each time even if it may offer a higher unit price since batch trading can save time and efforts in data collection. On the other hand, batch trading is profitable for mobile users since they can take advantage of volume premium. In this paper, we study how mobile users form a coalition to sell their sensing data together. Based on the concept of majorization, we propose a novel scheme to form a fair and stable coalition. Simulation results show the super performance of the proposed method compared with alternative solutions. In specific, the proposed scheme can improve the achieved utility and fairness by 623.68% and 5.51%, respectively, compared to the scheme with independent sell when the number of users is 90.

Compared with a traditional fixed sensor network, mobile crowdsensing provides an efficient way to collect sensing data. However, conducting sensing tasks consumes the resources of mobile users (e.g. battery, storage memory, time). Therefore, incentive mechanism design plays a key role in efficiently collecting the sensing data in a mobile crowdsensing system. Most of existing works about the incentive mechanism design simply use a constant to describe the data quality. In this study, the authors focus on the collection of video clips and introduce multiple parameters to evaluate the quality of the collected data. By jointly considering the social relationship of mobile users, they propose a social‐aware incentive mechanism to achieve the full‐view coverage for a target by efficiently collecting video clips. The proposed mechanism satisfies the properties of individual rationality, truthful and computational efficiency. Simulation results show better performance of the proposed mechanism compared with random selection and aspect based selection. In specific, with the number of users , the proposed mechanism can improve the data collector's utility by 485% and 33% compared with random selection and aspect based selection, respectively.

The authors take the quality of sensing data into consideration and design a reputation‐aware incentive mechanism (RAIM) with the properties of truthfulness and individual rationality while maximising the weighted social welfare of the whole system. In addition, in order to reduce the computational complexity of RAIM and improve the system feasibility, the authors propose a heuristic algorithm RAIM‐H, with the computational complexity of . Simulation results show the nice performance of the proposed mechanisms RAIM and RAIM‐H in terms of the weighted social welfare and the average reputation. Specifically, RAIM can improve the weighted social welfare by 8.65 and 48.16% compared with trustworthy sensing for crowd management (TSCM) and random selection, respectively, with the number of smartphone users . Meanwhile, RAIM‐H approaches to the maximum very well and can improve the weighted social welfare by 6.15% and 75% compared with TSCM and random selection, respectively, with the number of smartphone users .