IEEE Transactions on Broadcasting最新文献_第2页

Toward Multi-Dimensional Quality Assessment of AI-Generated Images via Prompt-Guided Relational Learning 基于即时引导关系学习的人工智能生成图像的多维质量评估

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-09-01 Epub Date: 2026-04-17 DOI: 10.1109/TBC.2026.3680037

Hangwei Chen;Feng Shao;Weiyi Jing;Xiaolong Tang;Xiongli Chai;Qiuping Jiang

{"title":"Toward Multi-Dimensional Quality Assessment of AI-Generated Images via Prompt-Guided Relational Learning","authors":"Hangwei Chen;Feng Shao;Weiyi Jing;Xiaolong Tang;Xiongli Chai;Qiuping Jiang","doi":"10.1109/TBC.2026.3680037","DOIUrl":"https://doi.org/10.1109/TBC.2026.3680037","url":null,"abstract":"The explosive growth of Artificial Intelligence Generated Images (AGIs) has created an urgent need for automated quality assessment. Unlike traditional image quality assessment, AGIs evaluation faces core challenges such as the absence of reference images, the need to integrate textual information, and the multi-dimensional nature of evaluation criteria. Existing methods typically rely on shared multimodal features for unified modeling, failing to effectively address two key issues: the differentiated extraction of dimension-specific features and the modeling of intrinsic relationships between dimensions. To address this, this paper proposes a Prompt-guided Relationship Learning Network (PRL-Net). Specifically, we first design a dimension-specific prompt attention (DPA) module, which introduces learnable prompts for each quality dimension to guide the model in adaptively focusing on dimension-related feature representations from shared features. Furthermore, we introduce a cross-dimensional graph relational attention (CGRA) module that explicitly models the complex dependencies between different quality dimensions (such as quality, consistency, and authenticity) through a dynamic graph neural network, enabling cross-dimensional feature interaction and information complementarity. Experimental results on multiple public benchmark datasets demonstrate that PRL-Net outperforms existing state-of-the-art methods across several evaluation metrics, validating its effectiveness.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"947-959"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GenRTC: Generative Real-Time Video Conferencing via Joint Adaptive Coding and Bandwidth Estimation 基于自适应编码和带宽估计的生成式实时视频会议

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-09-01 Epub Date: 2026-03-06 DOI: 10.1109/TBC.2026.3668547

Bingcong Lu;Jun Xu;Zhengxue Cheng;Rong Xie;Li Song;Wenjun Zhang

{"title":"GenRTC: Generative Real-Time Video Conferencing via Joint Adaptive Coding and Bandwidth Estimation","authors":"Bingcong Lu;Jun Xu;Zhengxue Cheng;Rong Xie;Li Song;Wenjun Zhang","doi":"10.1109/TBC.2026.3668547","DOIUrl":"https://doi.org/10.1109/TBC.2026.3668547","url":null,"abstract":"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>https://github.com/IreneLu12/GenRTC</uri>","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"975-989"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Transactions on Broadcasting Publication Information IEEE广播出版信息汇刊

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-09-01 Epub Date: 2026-09-04 DOI: 10.1109/TBC.2026.3726826

引用次数: 0

Joint Degradation-Aware Arbitrary-Scale Super-Resolution for Variable-Rate Extreme Image Compression 可变速率极端图像压缩的联合退化感知任意尺度超分辨率

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-09-01 Epub Date: 2026-04-06 DOI: 10.1109/TBC.2026.3680050

Xinning Chai;Zhengxue Cheng;Xin Li;Rong Xie;Li Song

{"title":"Joint Degradation-Aware Arbitrary-Scale Super-Resolution for Variable-Rate Extreme Image Compression","authors":"Xinning Chai;Zhengxue Cheng;Xin Li;Rong Xie;Li Song","doi":"10.1109/TBC.2026.3680050","DOIUrl":"https://doi.org/10.1109/TBC.2026.3680050","url":null,"abstract":"Recent diffusion-based extreme image compression methods have demonstrated remarkable performance at ultra-low bitrates (0.1 <0.1 bpp). However, most approaches require training separate diffusion models for each target bitrate, resulting in substantial computational overhead and hindering practical deployment. Meanwhile, recent studies have shown that joint super-resolution can serve as an effective approach for enhancing low-bitrate reconstruction. However, when moving toward ultra-low-bitrate regimes, these methods struggle due to severe information loss, and their reliance on fixed super-resolution scales prevents flexible adaptation across diverse bitrates. To address these limitations, we propose ASSR-EIC, a novel image compression framework that leverages arbitrary-scale super-resolution (ASSR) to support variable-rate extreme image compression (EIC). An arbitrary-scale downsampling module is introduced at the encoder side to provide controllable rate reduction, while a diffusion-based, joint degradation-aware ASSR decoder enables rate-adaptive reconstruction within a single model. We exploit the compression- and rescaling-aware diffusion prior to guide the reconstruction, yielding high-fidelity and high-realism restoration across diverse compression and rescaling settings. Specifically, we design a global compression–rescaling adaptor that offers holistic guidance for rate adaptation, and a local compression–rescaling modulator that dynamically balances generative and fidelity-oriented behaviors to achieve fine-grained, bitrate-adaptive detail restoration. To further enhance reconstruction quality, we introduce a dual semantic-enhanced design. Extensive experiments demonstrate that ASSR-EIC delivers state-of-the-art performance in extreme image compression while simultaneously supporting flexible bitrate control and adaptive rate-dependent reconstruction.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"904-919"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accurate Temporal Prior Modeling for Learned Video Compression 学习视频压缩的精确时间先验建模

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-09-01 Epub Date: 2026-06-10 DOI: 10.1109/TBC.2026.3698347

Dongjian Yang;Xiaopeng Fan;Debin Zhao

{"title":"Accurate Temporal Prior Modeling for Learned Video Compression","authors":"Dongjian Yang;Xiaopeng Fan;Debin Zhao","doi":"10.1109/TBC.2026.3698347","DOIUrl":"https://doi.org/10.1109/TBC.2026.3698347","url":null,"abstract":"Recently, learned video compression (LVC) has achieved remarkable progress. Accurate temporal prior modeling is crucial for the rate-distortion (RD) performance of LVC. However, existing methods often suffer from imprecise temporal priors due to limited reconstruction ability of frame decoders and insufficient exploitation of temporal information from reconstructed frames and features. To address these issues, we first design an enhanced feature reconstructor (EFR) by integrating content-aware depthwise separable convolution (CADSC) that excels at local modeling with efficient linear attention duality (ELAD) that facilitates complete global modeling. Based on the EFR, an enhanced frame decoder (EFD) is constructed to improve transform capability, thereby generating high-quality reconstructed frames and features. Furthermore, to fully mine temporal information from these high-quality reconstructions, we propose a multi-type temporal prior mixer (MTPM). Specifically, the MTPM fuses short-term temporal information obtained via optical-flow-based feature warping with long-term temporal information captured by a proposed state-update-based method, thereby further improving the accuracy of temporal prior modeling. The temporal prior produced by the MTPM not only provides high-quality conditional guidance for frame encoding and decoding, but is also combined with the hyper prior, spatial prior, and the discrete latent representation of the previous frame to construct a diverse prior fusion entropy model (DPFEM), which further enhances entropy estimation accuracy. Experimental results demonstrate that the proposed method outperforms existing LVC models and achieves an average performance gain of 29.64% compared with the LDB configuration of the H.266/VVC reference software VTM20.2.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"890-903"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Reinforcement Learning-Based Joint Phase Shift and Reflection Coefficient Optimization for RIS-Assisted Broadcast-Passive IoT Symbiotic Systems 基于深度强化学习的ris辅助广播无源物联网共生系统相移和反射系数联合优化

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-09-01 Epub Date: 2026-03-13 DOI: 10.1109/TBC.2026.3691658

Qiang Liu;Sen Xu;Hua Xu

{"title":"Deep Reinforcement Learning-Based Joint Phase Shift and Reflection Coefficient Optimization for RIS-Assisted Broadcast-Passive IoT Symbiotic Systems","authors":"Qiang Liu;Sen Xu;Hua Xu","doi":"10.1109/TBC.2026.3691658","DOIUrl":"https://doi.org/10.1109/TBC.2026.3691658","url":null,"abstract":"Broadcast-passive Internet of Things (IoT) symbiotic systems exploit ambient broadcast signals to simultaneously power passive devices and support their information transmission through backscatter communication. However, these systems still suffer from severe double-hop fading, limited coverage, and complex interference coordination between broadcast and passive IoT communications. This paper proposes a reconfigurable intelligent surface (RIS)-assisted system architecture and formulates a joint optimization problem of RIS phase shifts and passive device reflection coefficient to maximize passive IoT throughput, subject to broadcast receiver quality-of-service (QoS) and passive device energy harvesting constraints. Given the non-convex and coupled nature of the formulated problem, we develop a deep reinforcement learning (DRL) framework based on proximal policy optimization (PPO), incorporating a power-domain state space representation and a piecewise reward function to balance throughput maximization with multi-constraint satisfaction. Simulation results demonstrate that the proposed scheme achieves significant throughput gains with increasing RIS elements, while the PPO agent learns to push the operating point toward active constraint boundaries, achieving an effective balance among throughput maximization, broadcast QoS protection, and energy sustainability.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"826-835"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diffusion Bridge-Based Joint Estimation and Detection Design for Superimposed-Pilot OFDM Receiver 基于扩散桥的叠加导频OFDM接收机联合估计与检测设计

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-09-01 Epub Date: 2026-03-14 DOI: 10.1109/TBC.2026.3691647

Rui Zeng;Jingbo Tan;Xu Shi;Jintao Wang;Yifeng Zhang

{"title":"Diffusion Bridge-Based Joint Estimation and Detection Design for Superimposed-Pilot OFDM Receiver","authors":"Rui Zeng;Jingbo Tan;Xu Shi;Jintao Wang;Yifeng Zhang","doi":"10.1109/TBC.2026.3691647","DOIUrl":"https://doi.org/10.1109/TBC.2026.3691647","url":null,"abstract":"Superimposed-pilot (SP) transmission improves spectral efficiency but poses significant challenges for receiver design due to pilot–data interference, which conventional algorithms struggle to address effectively under fast-fading conditions. To address this issue, this paper proposes a diffusion bridge–based receiver for joint channel estimation and data detection. By formulating the received signals as one endpoint distribution and the transmitted data together with the channel impulse response as the other endpoint distribution, the conditional probability path between them is learned while implicitly exploiting OFDM structural constraints. To enable more stable and efficient training, the network is designed to directly predict the clean output, and the corresponding flow-velocity loss function for data detection is derived. Furthermore, a two-stage factor scheduler is introduced to balance the training objectives of channel estimation and data detection. Simulation results show that the proposed method achieves lower channel estimation mean square error (MSE) and data detection bit error rate (BER) than existing schemes, while exhibits sufficient generalization ability at higher subcarriers and modulation orders, demonstrating its effectiveness as a data-driven solution for reliable communication in dynamic environments.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"1024-1031"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LLM-Enhanced Transformer Self-Learning Framework for Dynamic Power Allocation in High-Throughput Satellite Communications 基于llm的高通量卫星通信动态功率分配变压器自学习框架

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-09-01 Epub Date: 2026-07-21 DOI: 10.1109/TBC.2026.3713270

Xiaoran Wang;Xin Hu;Letian Zhang;Shuaijun Liu;Fadhel M. Ghannouchi

{"title":"LLM-Enhanced Transformer Self-Learning Framework for Dynamic Power Allocation in High-Throughput Satellite Communications","authors":"Xiaoran Wang;Xin Hu;Letian Zhang;Shuaijun Liu;Fadhel M. Ghannouchi","doi":"10.1109/TBC.2026.3713270","DOIUrl":"https://doi.org/10.1109/TBC.2026.3713270","url":null,"abstract":"In high-throughput satellite (HTS) communication systems, dynamic power allocation is a core part of radio resource management, especially for supporting data-intensive applications that require reliable connectivity across wide geographic areas. However, existing solutions face notable limitations: traditional heuristic algorithms (e.g., PSO, GA) are constrained by high computational complexity and inability to leverage historical experience, while deep reinforcement learning (DRL) struggles with poor generalization to unseen request patterns and insufficient adaptability to dynamic traffic. To address these challenges, we propose an enhanced end-to-end self-learning framework for HTS dynamic power allocation. The framework first adopts a Large Language Model (LLM)-based module to parse user natural language requests, automatically extract and standardize key parameters, and intelligently schedule downstream processes. It then employs a hybrid optimization architecture: a Transformer model captures spatiotemporal correlations of traffic demands to generate high-quality initial power solutions, which are further refined by PSO-GA heuristic algorithms under power constraints to minimize the mismatch rate (MR). Additionally, an online self-learning mechanism based on Low-Rank Adaptation (LoRA) uses high-quality heuristic optimization results to incrementally fine-tune the Transformer, enhancing generalization without excessive computational overhead. Extensive simulations on a GEO HTS platform show that the proposed framework outperforms baseline methods (PSO, GA, DRL) in terms of allocation precision, cross-scenario generalization, and operational efficiency, effectively adapting to the dynamic demands of HTS power allocation.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"836-850"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148902940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Relevancy-Guided Adaptive Decoupling for Multi-Module Joint Optimization in Perceptual Video Coding 感知视频编码中多模块联合优化的关联引导自适应解耦

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-09-01 Epub Date: 2026-07-02 DOI: 10.1109/TBC.2026.3705501

Yushen Shi;Haibing Yin;Yiyan Xie;Xia Wang;Hongkui Wang;Xiaofeng Huang

{"title":"Relevancy-Guided Adaptive Decoupling for Multi-Module Joint Optimization in Perceptual Video Coding","authors":"Yushen Shi;Haibing Yin;Yiyan Xie;Xia Wang;Hongkui Wang;Xiaofeng Huang","doi":"10.1109/TBC.2026.3705501","DOIUrl":"https://doi.org/10.1109/TBC.2026.3705501","url":null,"abstract":"In professional broadcasting and Over-the-Top (OTT) streaming, non-linear coupling among perceptual modules in hybrid architectures fundamentally limits end-user Quality of Experience (QoE). Current heuristic tuning and brute-force searches cannot properly model cross-module dependencies. Encoders thus often fall into sub-optimal local minima. We propose a systematic relevancy-guided decoupling framework for perceptual video coding to address this issue. We introduce a quantitative Inter-module Relevancy Metric based on a full-factorial design. This metric explicitly models the interaction intensity among heterogeneous coding modules. Guided by this metric, our Influence-Based Ascending Optimization (IBAO) strategy transforms the NP-hard joint optimization into a deterministic sequential tuning path that mitigates cumulative errors. We also design a strictly bounded online adaptive parameter model to address video non-stationarity. It tracks local optimal operating points in real time with minimal runtime overhead. Experiments on the industrial HEVC/x265 platform validate an average 1.94 BD-VMAF gain over the standard x265 Slow preset. Critically, pixel-level objective fidelity remains undegraded. These results confirm our approach effectively resolves inter-module coupling conflicts. Ultimately, it delivers a stable, Pareto-optimal rate-distortion solution suited for real-world multimedia distribution.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 3","pages":"863-876"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148903196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Holistic Optimization for Immersive Services in Integrated Broadcast-Cellular Networks 广播蜂窝网络中沉浸式服务的整体优化

IF 5.9 1区计算机科学

IEEE Transactions on Broadcasting Pub Date : 2026-06-01 Epub Date: 2026-03-15 DOI: 10.1109/TBC.2026.3689316

Zhixin Liu;Wenjun Zhang;Dazhi He;Yin Xu;Shu Chen

{"title":"Holistic Optimization for Immersive Services in Integrated Broadcast-Cellular Networks","authors":"Zhixin Liu;Wenjun Zhang;Dazhi He;Yin Xu;Shu Chen","doi":"10.1109/TBC.2026.3689316","DOIUrl":"https://doi.org/10.1109/TBC.2026.3689316","url":null,"abstract":"The proliferation of immersive services, such as augmented and virtual reality, poses unprecedented challenges for wireless networks due to their stringent requirements for high data rates, low latency, and high reliability. This paper investigates the resource optimization problem in an integrated network architecture that combines a high-power high-tower (HPHT) broadcast network and low-power low-tower (LPLT) cellular networks for delivering multi-perspective immersive streams. We formulate a novel multi-objective optimization problem that simultaneously caters to the utilities of three key stakeholders: content providers (minimizing server congestion), network operators (balancing spectral and energy efficiency), and end users (maximizing quality of experience). To centrally coordinate resource allocation across both networks, we introduce an agent, Convergence Intelligent Processing Entity (CIPE), that collects information from all parties and makes globally optimized decisions. To tackle this complex, non-convex, and large-scale problem, we propose an efficient block coordinate descent (BCD)-based algorithm that decomposes the problem into tractable subproblems: HPHT parameter configuration, MBSFN zone formation and user association, and LPLT resource allocation. Extensive simulation results demonstrate that the proposed algorithm is significantly superior to several well-known baselines, including a single-cell point-to-point (SC-PTP) only scheme, an HPHT-only broadcast scheme, and various hybrid schemes that partially leverage heterogeneous resources, such as SC-PTP with broadcast, and SC-PTP with multi-point multicast. Furthermore, we established an external field experimental environment to verify the feasibility and performance gains of the integrated network architecture in a real-world setting.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"72 2","pages":"388-404"},"PeriodicalIF":5.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148433302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0