Delivering geographically segmented local content remains one of the key challenges in Single Frequency Networks (SFNs). By leveraging Layered Division Multiplexing (LDM), Local Content Insertion (LCI) can be achieved through Geographically Segmented Localcasting (GSL) for both multimedia and datacasting applications. This paper introduces a solution where region-wide broadcast services are carried by the Core Layer (CL) of the LDM, while local content is transmitted through the Enhancement Layer (EL). This approach also benefits network design and transmitter deployment, as new transmitters can be positioned freely within the intended broadcast service area. Each additional site enhances the reception quality of the regional component (LDM CL) while simultaneously delivering geographically specific multimedia and datacasting services to its local coverage zone. Since the localcasting signal on the EL operates at a lower transmission power, co-channel interference among local transmitters is minimized. A co-channel interference mitigation technique, Iterative Joint Detection and Decoding (IJDD), is also introduced. The paper outlines a roadmap for deploying LCI/GSL systems. Leveraging LDM enables any terrestrial television platform to adopt this approach in SFN or High-Power High-Tower (HPHT) configurations while remaining compatible with existing receivers. Furthermore, any cell-based wireless systems (5G/6G) can exploit this method, efficiently integrating broadcast, multicast, and unicast service delivery.

With the rapid advancement of sixth-generation (6G) wireless communications, broadcasting systems are evolving to support large-scale content distribution and real-time multi-user connectivity. Distributed computation has become a key enabler in this context, while growing concerns over data privacy, efficiency, and decentralized data sources have highlighted federated learning (FL) as a promising solution. In this work, we propose an FL framework supported by multiple intelligent reflecting surfaces (IRSs) and non-orthogonal multiple access (NOMA) to enhance the reliability and performance of broadcasting networks. IRSs are dynamically adapted to assist the transmission links between users and the base station (BS), thereby improving the accuracy of local model delivery and global model updates. To optimize the proposed system, we formulate a minimization problem targeting the FL loss function. We begin by deriving an analytical solutionfor the expected convergence behavior of the FL algorithm, capturing the impact of wireless channel conditions. Leveraging Jensen’s inequality, we transform the original problem into two iterative sub-problems, from which we obtain the optimal IRS phase shifts and user transmit power. Additionally, we introduce an auction-based IRS allocation algorithm to further reduce the FL loss. At the system level, we jointly exploit NOMA-enabled multi-user access and IRS-based dynamic channel enhancement, allowing more users to participate in FL through broadcasting channels with reduced packet loss, thereby improving model performance. Simulation results demonstrate that the proposed framework significantly outperforms existing benchmarks in reducing FL loss, while improving model accuracy and real-time convergence in broadcast-oriented wireless systems.

Efficient dynamic point cloud compression (DPCC) critically depends on accurate motion estimation and compensation. However, the inherently irregular structure and substantial local variations of point clouds make this task highly challenging. Existing approaches typically rely on explicit motion estimation, whose encoded motion vectors often fail to capture complex dynamics and inadequately exploit temporal correlations. To address these limitations, we propose a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Bidirectional Feature-aligned Motion Transformation (Bi-FMT)</b> framework that implicitly models motion in the feature space. Bi-FMT aligns features across both past and future frames to produce temporally consistent latent representations, which serve as predictive context in a conditional coding pipeline, forming a unified “Motion + Conditional” representation. Built upon this bidirectional feature alignment, we introduce a Cross-Transformer Refinement module (CTR) at the decoder side to adaptively refine locally aligned features. By modeling cross-frame dependencies with vector attention, CRT enhances local consistency and restores fine-grained spatial details that are often lost during motion alignment. Moreover, we design a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Random Access (RA)</b> reference strategy that treats the bidirectionally aligned features as conditional context, enabling frame-level parallel compression and eliminating the sequential encoding. Extensive experiments demonstrate that Bi-FMT surpasses <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D-DPCC</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AdaDPCC</b> in both compression efficiency and runtime, achieving BD-Rate reductions of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">20% (D1)</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9.4% (D1)</b>, respectively. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/dovedx/FMT_DPCC</uri>

A prevailing limitation of existing full-reference image quality assessment (FR-IQA) methods is their segregated focus on either local texture or global structure, preventing a unified analysis of these two critical and interrelated dimensions of human perception. To resolve this, we propose the Fisher Sinkhorn image quality assessment (FSIQA) framework, which establishes a principled unification of information geometry and optimal transport theory. FSIQA leverages a dual-branch architecture: its Fisher branch captures localized, nonGaussian texture distortions by modelling higher-order statistics on a Riemannian manifold, whereas the complementary Sinkhorn branch assesses global structural deformations via entropy-regularized optimal transport. This synergistic framework creates a mathematical continuum for local-to-global degradation analysis and is notably independent of the backbone network architecture, thus requiring no dataset-specific retraining. Comprehensive experiments across five benchmark datasets validate the efficacy of our approach, demonstrating that FSIQA consistently outperforms existing methods and establishes a new state-of-the-art method in the field.

Light Field Images (LFIs) capture both angular and spatial information, enabling immersive visual experiences for observers. However, during acquisition and processing, LFIs often suffer from various degradations, accompanied by intricate spatio-angular distortions. Consequently, objective LFI Quality Assessment (LFIQA) has garnered significant research interest. Existing blind LFIQA methods predominantly operate in a content-agnostic manner. They primarily model low-level distortions while neglecting the semantic context, a limitation that significantly diverges from the perception mechanisms of the Human Visual System (HVS). To bridge the gap between artifact analysis and scene understanding, we propose LV-BLiF, a novel dual-branch LFIQA network based on language-vision representation learning. Firstly, the textual prompts-assisted branch utilizes a pre-trained quality-aware Large Multimodal Model (LMM) to extract deep semantic representations, which are transformed into semantic context vectors sensitive to spatial-angular quality variations via pseudo-label guided learning strategy. Subsequently, semantic quality scores are generated based on this high-level information. Secondly, the visual cues-assisted branch employs disentangling convolutions to decompose light field features into spatial, angular, and epipolar plane image subspaces. These features are processed by Light Field Residual Blocks (LFRBs) and adaptively integrated via a Mixture-of-Experts-like (MoE-like) gating mechanism to predict visual quality scores. Finally, the semantic and visual scores are fused through a learnable adaptive gating mechanism to derive the overall LFI quality score. Extensive experiments on benchmark datasets, including NBU, SHU, and Win5-LID, demonstrate that LV-BLiF significantly outperforms state-of-the-art methods and exhibits high consistency with human perception. The proposed metric will be publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/shuaibilx/LV-BLiF</uri>

New digital television standards are emerging to meet the demand for new services from TV broadcasters, as well as to support new high-resolution audio and video formats available on the market. In Brazil, the Brazilian Digital TV System (SBTVD) Forum defines the standard to be adopted. Among the currently available physical layer technologies, the Advanced Television Systems Committee (ATSC) 3.0 standard offers great flexibility in operating modes, depending on the broadcasters’ requirements and propagation conditions, for both fixed and mobile reception. This allows the system to be configured to balance robustness and transmission efficiency. This paper presents the performance test results in various mobile reception scenarios in the city of Rio de Janeiro, using an ATSC 3.0 signal with a transmission rate of 32.3 Mbps in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\times 2$</tex-math> </inline-formula> Multiple-Input Multiple-Output (MIMO) configuration. The results showed a reception success rate of 53.6%, with an increase in reception errors for Carrier-to-Noise Ratio (C/N) lower than 26 dB. Furthermore, reception errors were detected with greater intensity when the received signal level was below -70 dBm. Although the overall success rate was around 50%, the complementary metrics (C/N threshold, RSSI, and PLP0 error behavior) were consistent with theoretical expectations, demonstrating that ATSC 3.0 performs effectively under real urban mobility conditions at high spectral efficiency. ATSC 3.0 thus proved effective in broadcast transmission, meeting the needs for ultra-high-definition (UHD) audio and video broadcasting.

Video-based point cloud compression (V-PCC) has achieved remarkable performance in dynamic point cloud compression. However, due to the 3D-to-2D projection in V-PCC, the temporal continuity across projected video frames is broken, limiting inter-frame coding efficiency. In this paper, we propose a virtual reference-based predictive coding (VRPC) scheme for V-PCC attribute compression to improve coding efficiency. This coding scheme uses the temporal correspondence between point clouds to guide the alignment of attribute video frames for virtual reference frame generation. Specifically, we first construct a neural deformation field-based geometry alignment module to align the reference and target point clouds to reduce the effect of motion on temporal correspondence estimation. Then, temporal correspondence is obtained in 3D space and used to align the reference and target video frames to generate the occupied region of the virtual reference frame. Finally, after padding the unoccupied region, the resulting reference frame is added to the reference picture list and used by the target frame for inter-frame coding. Experimental results demonstrate that the proposed method effectively improves the attribute coding efficiency of V-PCC.

This article presents a technical analysis of the influ-ence of polarization on terrestrial reception in the TV 3.0 system, also known commercially as DTV+, focusing on the evaluation of polarization effects and their influence over the robustness influence on the robustness of Multiple-Input Multiple-Output (MIMO) operation through polarization diversity. Urban scenarios with different Line-of-Sight (LoS) conditions and two reception environments, outdoor and indoor, were modeled to assess the relative performance of Vertical (V) and Horizontal (H) linear polarizations (0° and 90°), as well as slant polarization (-45° and +45°). The methodology combines vector modeling of the electromagnetic field with parametric simulations based on distance, obstruction, and angular dispersion. The results highlight an asymmetry between V and H polarizations. Horizontal polarization shows superior performance in outdoor environments, while ±45° polarization demonstrated intermediate performance and greater vector stability. The analysis of the Effective Polarization Ratio (EPR) proved decisive in assessing reception robustness. This metric was particularly relevant in indoor reception, where vector degradation can compromise the separation of MIMO streams even at adequate power levels. It is concluded that the choice of polarization should take into account factors such as distance, obstruction, and the predominant reception type. The ±45° configuration is technically interesting due to its balance between robustness, selectivity, and coverage.

Real-time video broadcasting has become increasingly prevalent, enabling wide-scale distribution of video content to diverse audiences. However, network conditions often remain volatile, particularly in bandwidth-constrained environments such as subways or congested public areas, where both broadcasting and real-time communication (RTC) systems struggle to maintain optimal performance. In this paper, we propose GenRTC, a generative video conferencing system designed to mitigate bandwidth constraints by integrating joint scalable generative face coding and network-aware adaptation. This approach significantly improves the system’s robustness against network volatility, guaranteeing low-latency and high-fidelity video conferencing across a broad spectrum of bandwidth scenarios. GenRTC integrates conventional video codecs and generative face video encoding within a latency–quality joint adaptive codec controller, which dynamically selects the most appropriate encoding scheme to maximize perceptual quality under the latency constraints of real-time applications across diverse network conditions. In addition, GenRTC incorporates a bandwidth prediction module that delivers accurate and temporally stable low-bandwidth estimation. Experimental results demonstrate that GenRTC achieves a good latency-quality trade-off. GenRTC reduces frame timeouts from 60.4% to 5.4% on average under 200 Kbps. At a similar missrate, it improves PSNR by 7.2 dB. Specifically, at 20 Kbps, GenRTC achieves an 8% missrate and 32dB PSNR, while traditional RTC systems cannot operate at such ultra-low bandwidth. To facilitate a more intuitive comparison of perceptual quality, demo videos are provided at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/IreneLu12/GenRTC</uri>