Portable volumetric video capturing systems present a compelling alternative to traditional, bulky prototype systems used for streaming and interacting with volumetric content. Their key advantages, particularly flexibility and ease of deployment, make them suitable for a wide range of applications. However, despite their potential, there has been limited exploration into the design of such portable systems. This paper addresses this gap by conducting an in-depth analysis of portability and proposing an optimized camera array configuration tailored for high-quality volumetric content generation. Our approach begins with a novel, flexible camera calibration method that leverages geometric priors, enabling accurate alignment of multiple cameras without requiring specialized expertise. Building on this, we introduce a meticulous fusion technique that integrates captured and inferred data to reconstruct complete volumetric representations. This method achieves a fusion latency of less than 100 ms, ensuring real-time performance. We integrate these innovations into a portable capturing system, named PortaCap, which incorporates a carefully designed camera deployment strategy. Through both quantitative and qualitative evaluations, PortaCap demonstrates significant improvements in volumetric content quality, achieving enhancements ranging from 13% to 33.8%. These results underscore the system's potential to advance the state-of-the-art in portable volumetric video capture.

Ubiquitous connectivity is a key 6G usage scenario, in which large-scale sensing systems deployed in remote and underserved regions must deliver heterogeneous sensing data under stringent energy budgets and deadline constraints. This paper presents EcoPath, a two-tier data aggregation framework for clustered large-scale sensor networks. EcoPath separates low-power intra-cluster collection from a high-rate multi-interface backhaul operated by cluster heads, where Multipath QUIC (MPQUIC) can be practically deployed to exploit path diversity. At the cluster head, EcoPath jointly integrates (i) a deadline-aware bundling controller that aggregates sensor frames into MTU-bounded bundles to amortize protocol overhead while bounding additional waiting time, and (ii) a robust multi-path scheduler that prioritizes packets using Weighted Earliest- Deadline-First (W-EDF) with fairness protection and selects backhaul paths via a stability-aware quality metric with hysteresis to avoid flapping under time-varying links. We further formulate an explicit energy–timeliness optimization and show how its outputs parameterize the online bundling and scheduling policies. Extensive simulations with realistic wireless effects, together with baselines and ablations, demonstrate that EcoPath improves energy efficiency and deadline satisfaction for large-scale aggregation.

The gradual maturation of mobile augmented reality (AR) and localization technologies is enabling the development of immersive AR-enabled indoor localization and navigation systems. Existing indoor localization technologies (e.g., WiFi, infrared, Bluetooth) and navigation services do not provide intuitive 3D AR experiences and can be expensive to deploy. This paper introduces WebARNav, a cross-platform indoor localization system that provides user-friendly AR navigation services with low overhead and remarkable accuracy. First, we propose a lightweight location fusion framework for indoor navigation on the mobile web, which leverages accurate edge-supported vision localization to guide and correct lightweight pedestrian dead reckoning localization. Second, we improve the accuracy of localization using an attention-based feature extraction method and a dual-stream retrieval and co-visibility re-ranking technique for initial localization. Third, we significantly improve accuracy and speed up retrieval as users move by generating a topological map for traveling localization. We conducted extensive experiments on various indoor datasets to demonstrate localization accuracy and navigation experience. The study shows that WebARNav achieves a localization frequency of over 30 Hz and reduces the average trajectory error by 76% and 95% for single- and multi-floor office scenes, respectively, compared to the PDR-only method. The proposed traveling localization method also reduces the localization latency by 15.2%, 55.1%, and 98.6% in the baseline datasets, with an accuracy improvement of over 4%.

Mobile Augmented Reality (MAR) applications pose unique challenges due to computation intensity, constrained device resources, and high interactive rendering requirements. The emergence of 5 G and edge computing offers opportunities to offload computation to the edge and cloud, indirectly enhancing the computing capability and usage duration of MAR devices. However, existing general task offloading and multipath transmission techniques do not address the challenges in offloading path selection with multiple edges, dynamic resource competition awareness, and spatial computation with strong task dependencies. This paper contributes FPSelector, a flexible path selector for MAR offloading. We present a two-tier MAR-specific offloading scheme with multiple edge nodes. In offloading decisions, we design a reinforcement learning model to generate the selection policy for each packet of an AR data stream. This model incorporates an action masking mechanism, a comprehensive reward function, and state features complemented by a resource prediction module, making FPSelector aware of dynamic heterogeneous environments. Moreover, we propose an online learning strategy to facilitate real-time selection. To validate its efficacy, we compare FPSelector's performance against leading schedulers under various scenarios, demonstrating a notable reduction of 9.9% and 9.6% in overall completion time for 4 K and 8 K video-based MAR applications compared to its closest competitor.

In the metaverse era, point cloud video (PCV) streaming on mobile XR devices is pivotal. While most current methods focus on PCV compression from traditional 3-DoF video services, emerging AI techniques extract vital semantic information, producing content resembling the original. However, these are early-stage and computationally intensive. To enhance the inference efficacy of AI-based approaches, accommodate dynamic environments, and facilitate applicability to metaverse XR devices, we present ISCom, an interest-aware semantic communication scheme for lightweight PCV streaming. ISCom is featured with a region-of-interest (ROI) selection module, a lightweight encoder-decoder training module, and a learning-based scheduler to achieve real-time PCV decoding and rendering on resource-constrained devices. ISCom’s dual-stage ROI selection provides significantly reduces data volume according to real-time interest. The lightweight PCV encoder-decoder training is tailored to resource-constrained devices and adapts to the heterogeneous computing capabilities of devices. Furthermore, We provide a deep reinforcement learning (DRL)-based scheduler to select optimal encoder-decoder model for various devices adaptivelly, considering the dynamic network environments and device computing capabilities. Our extensive experiments demonstrate that ISCom outperforms baselines on mobile devices, achieving a minimum rendering frame rate improvement of 10 FPS and up to 22 FPS. Furthermore, our method significantly reduces memory usage by 41.7% compared to the state-of-the-art AITransfer method. These results highlight the effectiveness of ISCom in enabling lightweight PCV streaming and its potential to improve immersive experiences for emerging metaverse application.

Mobile video streaming enables flexible delivery of videos to mobile devices, supporting emerging video formats. The transition from conventional 2D videos to immersive formats, such as virtual reality and holographic videos, significantly increases the demand for computation and network resources. Existing streaming techniques are predominantly developed for specific video types, neglecting fair adaptive transmission and optimal resource utilization in services involving multiple video types. This paper investigates hybrid video streaming, encompassing 2D, 360-degree, and volumetric videos. To accommodate resource-intensive hybrid video streaming on mobile devices, we proposeHiVAT, an adaptive transcoding-based system that ensures Quality of Experience (QoE) for each stream type. We contribute 1) a transcoding-based framework to address the challenges of high bandwidth and decoding overhead on mobile devices; 2) a universal QoE model involving traditional factors, viewport smoothness, degree of immersion, etc., for transcoded video streams; 3) a multi-agent adaptive bitrate controller that collaboratively determines hybrid video quality levels to achieve high and fair QoE across multiple streams; and 4) a learning-based task scheduler to optimize computation resource usage, thereby improving the overall serviceability of the system. We evaluateHiVATagainst state-of-the-art methods, witnessing an average QoE improvement of 5.9% and 9.9% on linear and logarithmic metrics, respectively.

Undersampling is one of the most popular techniques for dealing with class-imbalance problems. Various undersampling methods have emerged over the past few decades. Each of them exhibits the superiority in some scenarios. However, selecting representative majority-class samples such that the structures of the selected groups are maintained according to the underlying imbalanced distribution remains a challenge. For this purpose, this paper proposes Spatial Distribution-based UnderSampling (SDUS) for imbalanced learning. SDUS uses a supervised constructive process to learn majority-class local patterns in terms of sphere neighborhoods (SPN). Two sample selection strategies, specifically, a top-down strategy and a bottom-up strategy, are proposed for maintaining the distribution pattern of original data in selecting majority-class sample subsets from different perspectives. SDUS introduces an ensemble technique that improves learning performance by utilizing the diversity caused by the randomness of the local-pattern learning process. Numerical experiments on 38 typical datasets from KEEL repository and 13 state-of-the-art comparison methods demonstrate the effectiveness of SDUS in maintaining the underlying distribution characteristics for imbalanced undersampling.

Volumetric video provides a more immersive holographic virtual experience than conventional video services such as 360-degree and virtual reality (VR) videos. However, due to ultra-high bandwidth requirements, existing compression and transmission technology cannot handle the delivery of real-time volumetric video. Unlike traditional compression methods and the approaches that extend 360-degree video streaming, we propose AITransfer, an AI-powered compression and semantic-aware transmission method for point cloud video data (a popular volumetric data format). AITransfer targets the semantic-level communication beyond transmitting raw point cloud video or compressed video with two outstanding contributions: (1) designing an integrated end-to-end architecture with two fundamental contents of feature extraction and reconstruction to reduce the bandwidth consumption and alleviate the computational pressure; and (2) incorporating the dynamic network condition into end-to-end architecture design and employing a deep reinforcement learning-based adaptive control scheme to provide robust transmission. We conduct extensive experiments on the typical datasets and develop a case study to demonstrate the efficiency and effectiveness. The results show that AITransfer can provide extremely efficient point cloud transmission while maintaining considerable user experience with more than 30.72x compression ratio under the existing network environments.

Point cloud video provides a more immersive holographic virtual experience than conventional video services such as 360 degree video and virtual reality (VR) video. However, the existing network bandwidth and transmission technology can not carry real-time point cloud video streaming due to mass data volume, high processing overheads, and extremely bandwidth-consuming. Unlike previous approaches that extend the VR video streaming, we propose AITransfer, an AI-powered bandwidth-aware and adaptive transmission technique driven by extracting and transferring key point cloud features to reduce the bandwidth consumption and alleviate the computational pressure. AITransfer has two outstanding contributions, including (1) incorporating the dynamic network bandwidth into the design of an end-to-end architecture with two fundamental contents of feature extraction and reconstruction, and (2) employing an online adapter to sense the network bandwidth and match the optimal inference model. We conduct extensive experiments on the typical dataset and develop a case study to demonstrate the efficiency and effectiveness. The results show that AITransfer can provide more than 30.72 times compression ratio under the existing network environments.