In modern video coding standards, block-based inter prediction is widely adopted, which brings high compression efficiency. However, in natural videos, there are usually multiple moving objects of arbitrary shapes, resulting in complex motion fields that are difficult to represent compactly. This problem has been tackled by more flexible block partitioning methods in the Versatile Video Coding (VVC) standard, but the more flexible partitions require more overhead bits to signal and still cannot be made arbitrarily shaped. To address this limitation, we propose an object segmentation-assisted inter prediction method (SAIP), where objects in the reference frames are segmented by some advanced technologies. With a proper indication, the object segmentation mask is translated from the reference frame to the current frame as the arbitrary-shaped partition of different regions without any extra signal. Using the segmentation mask, motion compensation is separately performed for different regions, achieving higher prediction accuracy. The segmentation mask is further used to code the motion vectors of different regions more efficiently. Moreover, the segmentation mask is considered in the joint rate-distortion optimization for motion estimation and partition estimation to derive the motion vector of different regions and partition more accurately. The proposed method is implemented into the VVC reference software, VTM version 12.0. Experimental results show that the proposed method achieves up to 1.98%, 1.14%, 0.79%, and on average 0.82%, 0.49%, 0.37% BD-rate reduction for common test sequences, under the Low-delay P, Low-delay B, and Random Access configurations, respectively.
{"title":"Object Segmentation-Assisted Inter Prediction for Versatile Video Coding","authors":"Zhuoyuan Li;Zikun Yuan;Li Li;Dong Liu;Xiaohu Tang;Feng Wu","doi":"10.1109/TBC.2024.3434520","DOIUrl":"10.1109/TBC.2024.3434520","url":null,"abstract":"In modern video coding standards, block-based inter prediction is widely adopted, which brings high compression efficiency. However, in natural videos, there are usually multiple moving objects of arbitrary shapes, resulting in complex motion fields that are difficult to represent compactly. This problem has been tackled by more flexible block partitioning methods in the Versatile Video Coding (VVC) standard, but the more flexible partitions require more overhead bits to signal and still cannot be made arbitrarily shaped. To address this limitation, we propose an object segmentation-assisted inter prediction method (SAIP), where objects in the reference frames are segmented by some advanced technologies. With a proper indication, the object segmentation mask is translated from the reference frame to the current frame as the arbitrary-shaped partition of different regions without any extra signal. Using the segmentation mask, motion compensation is separately performed for different regions, achieving higher prediction accuracy. The segmentation mask is further used to code the motion vectors of different regions more efficiently. Moreover, the segmentation mask is considered in the joint rate-distortion optimization for motion estimation and partition estimation to derive the motion vector of different regions and partition more accurately. The proposed method is implemented into the VVC reference software, VTM version 12.0. Experimental results show that the proposed method achieves up to 1.98%, 1.14%, 0.79%, and on average 0.82%, 0.49%, 0.37% BD-rate reduction for common test sequences, under the Low-delay P, Low-delay B, and Random Access configurations, respectively.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1236-1253"},"PeriodicalIF":3.2,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141940835","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}
Pub Date : 2024-08-01DOI: 10.1109/TBC.2024.3434676
Wen-Xuan Long;Nian Li;Yuan Liu;M. R. Bhavani Shankar;Rui Chen
This paper considers the issue of acquiring channel state information (CSI) for multi-user orbital angular momentum (MU-OAM) wireless backhaul between the macro base station (MBS) and small base stations (SBSs) within broadcasting networks. Unlike prior works, we assume that each SBS transmits a pilot signal of length one on each multiplexed OAM mode and subcarrier, resulting in the coherent observations collected at the MBS. Then, we construct the data sets using the coherent observations, the components of which independently contain arbitrarily assumed positional information. The amplitude-phase multiple signal classification (AP-MUSIC) algorithm, a novel variant of the MUSIC, then conducts a two-dimensional (2-D) search on the amplitude and phase of the data component in both the OAM mode and frequency domains for estimating positions at each iteration. These estimates, together with the observations, are used to iteratively update the data sets, ultimately refining the distances and AoAs of all SBSs. The theoretical analysis and simulation results indicate that this solution not only yields the precise CSI for the MU-OAM system, but also markedly reduces the training overhead, compared to existing alternatives.
{"title":"Low-Overhead Iterative Channel Parameter Estimation for Multi-User OAM Wireless Backhaul","authors":"Wen-Xuan Long;Nian Li;Yuan Liu;M. R. Bhavani Shankar;Rui Chen","doi":"10.1109/TBC.2024.3434676","DOIUrl":"10.1109/TBC.2024.3434676","url":null,"abstract":"This paper considers the issue of acquiring channel state information (CSI) for multi-user orbital angular momentum (MU-OAM) wireless backhaul between the macro base station (MBS) and small base stations (SBSs) within broadcasting networks. Unlike prior works, we assume that each SBS transmits a pilot signal of length one on each multiplexed OAM mode and subcarrier, resulting in the coherent observations collected at the MBS. Then, we construct the data sets using the coherent observations, the components of which independently contain arbitrarily assumed positional information. The amplitude-phase multiple signal classification (AP-MUSIC) algorithm, a novel variant of the MUSIC, then conducts a two-dimensional (2-D) search on the amplitude and phase of the data component in both the OAM mode and frequency domains for estimating positions at each iteration. These estimates, together with the observations, are used to iteratively update the data sets, ultimately refining the distances and AoAs of all SBSs. The theoretical analysis and simulation results indicate that this solution not only yields the precise CSI for the MU-OAM system, but also markedly reduces the training overhead, compared to existing alternatives.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"71 1","pages":"74-80"},"PeriodicalIF":3.2,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10620284","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-31DOI: 10.1109/TBC.2024.3434536
Jian Wang;Yulong Hao;Zhongle Wu;Yafei Shi;Cheng Yang
Frequency modulation (FM) broadcasting is a robust and widely applied technology that offers unparalleled advantages over other broadcasting methods in challenging environments. In order to achieve high accuracy in constructing broadcasting maps for scenarios with uneven and sparse distribution of measurement data, we introduce the concept of FM broadcasting maps and propose a novel methodology for their construction. This paper utilizes the Long Short-Term Memory (LSTM) model to assimilate predictions from the ITU-R models for modeling purposes. To begin, we analyzed critical environmental parameters influencing radio wave propagation. Based on this analysis, we identified the foundational input features for the LSTM model. Subsequently, predictions from the ITU-R P.1546 and 2001 models were assimilated as features and input into the LSTM model for training, resulting in assimilation modeling. Finally, a broadcast map is constructed using the parameter construction method based on the proposed model. The results indicate that the relative error between the measurements and the proposed models, ITU-R P.1546 and ITU-R P.2001, are 3.14%, 6.48%, and 9.89%, respectively. The prediction accuracy of the proposed model surpasses that of the ITU-R models, and stability is significantly improved compared to models solely based on LSTM. The broadcast map in this paper provides an objective reflection of measured field strength values across multiple dimensions, including frequency, distance, various terrains, and error distribution. It demonstrates notable advantages in scenarios characterized by sparse and unevenly distributed sampling points.
{"title":"A Broadcast Map Constructing Method Based on the LSTM and Assimilation Theory","authors":"Jian Wang;Yulong Hao;Zhongle Wu;Yafei Shi;Cheng Yang","doi":"10.1109/TBC.2024.3434536","DOIUrl":"10.1109/TBC.2024.3434536","url":null,"abstract":"Frequency modulation (FM) broadcasting is a robust and widely applied technology that offers unparalleled advantages over other broadcasting methods in challenging environments. In order to achieve high accuracy in constructing broadcasting maps for scenarios with uneven and sparse distribution of measurement data, we introduce the concept of FM broadcasting maps and propose a novel methodology for their construction. This paper utilizes the Long Short-Term Memory (LSTM) model to assimilate predictions from the ITU-R models for modeling purposes. To begin, we analyzed critical environmental parameters influencing radio wave propagation. Based on this analysis, we identified the foundational input features for the LSTM model. Subsequently, predictions from the ITU-R P.1546 and 2001 models were assimilated as features and input into the LSTM model for training, resulting in assimilation modeling. Finally, a broadcast map is constructed using the parameter construction method based on the proposed model. The results indicate that the relative error between the measurements and the proposed models, ITU-R P.1546 and ITU-R P.2001, are 3.14%, 6.48%, and 9.89%, respectively. The prediction accuracy of the proposed model surpasses that of the ITU-R models, and stability is significantly improved compared to models solely based on LSTM. The broadcast map in this paper provides an objective reflection of measured field strength values across multiple dimensions, including frequency, distance, various terrains, and error distribution. It demonstrates notable advantages in scenarios characterized by sparse and unevenly distributed sampling points.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"924-934"},"PeriodicalIF":3.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141869492","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}
Pub Date : 2024-07-31DOI: 10.1109/TBC.2024.3420748
Zean Chen;Yeyao Chen;Gangyi Jiang;Mei Yu;Haiyong Xu;Ting Luo
Light Field (LF) imaging captures the spatial and angular information of light rays in the real world and enables various applications, including digital refocusing and single-shot depth estimation. Unfortunately, due to the limited sensor size of LF cameras, the captured LF images suffer from low spatial resolution while providing a dense angular sampling. Existing single-input LF spatial super-resolution (SR) methods usually utilize the inherent sub-pixel information to recover high-frequency textures, but they struggle in large-scale SR tasks (e.g., $8times $ ). Conversely, the heterogeneous imaging approach combining an LF camera and a 2D digital camera can capture richer information for effective large-scale reconstruction. To this end, this paper proposes a multi-scale spatial-angular collaborative guidance network (LF-MSACGNet) for heterogeneous LF spatial SR. Specifically, a context-guided deformable alignment module is first designed, which utilizes high-level feature information to achieve precise alignment between the low-resolution LF image and the 2D high-resolution image. Subsequently, a Transformer-driven spatial-angular collaborative guidance module is constructed to explore the spatial-angular correlation and complementarity. This allows for an effective fusion of the multi-resolution spatial-angular features. Finally, the SR LF image is reconstructed through a spatial-angular aggregation module. In addition, a multi-scale training strategy is adopted to subdivide the challenging large-scale SR task into multiple simple tasks to boost the SR performance. Experimental results on seven public datasets show that the proposed method outperforms the state-of-the-art SR methods in both quantitative and qualitative comparison, and exhibits favorable robustness to wide baseline LF images.
{"title":"Multi-Scale Spatial-Angular Collaborative Guidance Network for Heterogeneous Light Field Spatial Super-Resolution","authors":"Zean Chen;Yeyao Chen;Gangyi Jiang;Mei Yu;Haiyong Xu;Ting Luo","doi":"10.1109/TBC.2024.3420748","DOIUrl":"10.1109/TBC.2024.3420748","url":null,"abstract":"Light Field (LF) imaging captures the spatial and angular information of light rays in the real world and enables various applications, including digital refocusing and single-shot depth estimation. Unfortunately, due to the limited sensor size of LF cameras, the captured LF images suffer from low spatial resolution while providing a dense angular sampling. Existing single-input LF spatial super-resolution (SR) methods usually utilize the inherent sub-pixel information to recover high-frequency textures, but they struggle in large-scale SR tasks (e.g., \u0000<inline-formula> <tex-math>$8times $ </tex-math></inline-formula>\u0000). Conversely, the heterogeneous imaging approach combining an LF camera and a 2D digital camera can capture richer information for effective large-scale reconstruction. To this end, this paper proposes a multi-scale spatial-angular collaborative guidance network (LF-MSACGNet) for heterogeneous LF spatial SR. Specifically, a context-guided deformable alignment module is first designed, which utilizes high-level feature information to achieve precise alignment between the low-resolution LF image and the 2D high-resolution image. Subsequently, a Transformer-driven spatial-angular collaborative guidance module is constructed to explore the spatial-angular correlation and complementarity. This allows for an effective fusion of the multi-resolution spatial-angular features. Finally, the SR LF image is reconstructed through a spatial-angular aggregation module. In addition, a multi-scale training strategy is adopted to subdivide the challenging large-scale SR task into multiple simple tasks to boost the SR performance. Experimental results on seven public datasets show that the proposed method outperforms the state-of-the-art SR methods in both quantitative and qualitative comparison, and exhibits favorable robustness to wide baseline LF images.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1221-1235"},"PeriodicalIF":3.2,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141869491","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}
Pub Date : 2024-07-16DOI: 10.1109/TBC.2024.3419564
Yuki Koizumi;Yoichi Suzuki;Masashi Kamei
We are developing a diverse reception system (DRS) to compensate for rain attenuation in satellite broadcasting that utilizes path diversity between a satellite channel and a best-effort (BE)-IP network for end users. The proposed DRS recovers signals lost on a satellite channel due to significant rain attenuation by supplementing them via a BE-IP network. The unique point of the DRS is that it uses a common forward error correction (FEC) for both channels by applying a FEC for satellite broadcasting to the error correction in BE-IP network. This allows both channels to be integrated at the FEC layer and improves the quality of service (QoS) in the satellite broadcasting thanks to sharing the decoding information between the two. In this paper, we propose an advanced error-correction technique that achieves a high QoS in the DRS by sharing each log-likelihood ratio (LLR) utilized for the FEC decoding of bits received over both channels. This technique, which we call the LLR sharing method, enables successful error correction even when neither a satellite channel nor a BE-IP network can individually achieve error-free transmission due to significant rain attenuation or burst packet loss, respectively. Computer simulation results confirm that the LLR sharing method can improve the required C/N in the satellite broadcasting. In addition, we discuss how to suppress the load of the BE-IP network and integrate both channels efficiently.
{"title":"Improving QoS of Satellite Broadcasting Against Rain Attenuation by LLR Sharing Method With IP Network Integration at FEC Layer","authors":"Yuki Koizumi;Yoichi Suzuki;Masashi Kamei","doi":"10.1109/TBC.2024.3419564","DOIUrl":"10.1109/TBC.2024.3419564","url":null,"abstract":"We are developing a diverse reception system (DRS) to compensate for rain attenuation in satellite broadcasting that utilizes path diversity between a satellite channel and a best-effort (BE)-IP network for end users. The proposed DRS recovers signals lost on a satellite channel due to significant rain attenuation by supplementing them via a BE-IP network. The unique point of the DRS is that it uses a common forward error correction (FEC) for both channels by applying a FEC for satellite broadcasting to the error correction in BE-IP network. This allows both channels to be integrated at the FEC layer and improves the quality of service (QoS) in the satellite broadcasting thanks to sharing the decoding information between the two. In this paper, we propose an advanced error-correction technique that achieves a high QoS in the DRS by sharing each log-likelihood ratio (LLR) utilized for the FEC decoding of bits received over both channels. This technique, which we call the LLR sharing method, enables successful error correction even when neither a satellite channel nor a BE-IP network can individually achieve error-free transmission due to significant rain attenuation or burst packet loss, respectively. Computer simulation results confirm that the LLR sharing method can improve the required C/N in the satellite broadcasting. In addition, we discuss how to suppress the load of the BE-IP network and integrate both channels efficiently.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"822-832"},"PeriodicalIF":3.2,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10599863","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141720316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-09DOI: 10.1109/TBC.2024.3414656
Rafael Martínez;Álvaro Llorente;Alberto del Rio;Javier Serrano;David Jimenez
The evolution of telecommunication networks unlocks new possibilities for multimedia services, including enriched and personalized experiences. However, ensuring high Quality of Service and Quality of Experience requires intelligent solutions at the edge. This study investigates the real-time detection of race bib numbers using YOLOv8, a state-of-the-art object detection framework, within the context of 5G/6G edge computing. We train (BDBD and SVHN datasets) and analyze various YOLOv8 models (nano to extreme) across two diverse racing datasets (TGCRBNW and RBNR), encompassing varied environmental conditions (daytime and nighttime). Our assessment focuses on key performance metrics, including processing time, efficiency, and accuracy. For instance, on the TGCRBNW dataset, the extreme-sized model shows a noticeable reduction in prediction time when the more powerful GPU is used, with times decreasing from 1,161 to 54 seconds on a desktop computer. Similarly, on the RBNR dataset, the extreme-sized model exhibits a significant reduction in prediction time from 373 to 15 seconds when using the more powerful GPU. In terms of accuracy, we found varying performance across scenarios and datasets. For example, not good enough results are obtained in most scenarios on the TGCRBNW dataset (lower than 50% in all sets and models), while YOLOv8m obtain the high accuracy in several scenarios on the RBNR dataset (almost 80% of accuracy in the best set). Variability in prediction times was observed between different computer architectures, highlighting the importance of selecting appropriate hardware for specific tasks. These results emphasize the importance of aligning computational resources with the demands of real-world tasks to achieve timely and accurate predictions.
{"title":"Performance Evaluation of YOLOv8-Based Bib Number Detection in Media Streaming Race","authors":"Rafael Martínez;Álvaro Llorente;Alberto del Rio;Javier Serrano;David Jimenez","doi":"10.1109/TBC.2024.3414656","DOIUrl":"10.1109/TBC.2024.3414656","url":null,"abstract":"The evolution of telecommunication networks unlocks new possibilities for multimedia services, including enriched and personalized experiences. However, ensuring high Quality of Service and Quality of Experience requires intelligent solutions at the edge. This study investigates the real-time detection of race bib numbers using YOLOv8, a state-of-the-art object detection framework, within the context of 5G/6G edge computing. We train (BDBD and SVHN datasets) and analyze various YOLOv8 models (nano to extreme) across two diverse racing datasets (TGCRBNW and RBNR), encompassing varied environmental conditions (daytime and nighttime). Our assessment focuses on key performance metrics, including processing time, efficiency, and accuracy. For instance, on the TGCRBNW dataset, the extreme-sized model shows a noticeable reduction in prediction time when the more powerful GPU is used, with times decreasing from 1,161 to 54 seconds on a desktop computer. Similarly, on the RBNR dataset, the extreme-sized model exhibits a significant reduction in prediction time from 373 to 15 seconds when using the more powerful GPU. In terms of accuracy, we found varying performance across scenarios and datasets. For example, not good enough results are obtained in most scenarios on the TGCRBNW dataset (lower than 50% in all sets and models), while YOLOv8m obtain the high accuracy in several scenarios on the RBNR dataset (almost 80% of accuracy in the best set). Variability in prediction times was observed between different computer architectures, highlighting the importance of selecting appropriate hardware for specific tasks. These results emphasize the importance of aligning computational resources with the demands of real-world tasks to achieve timely and accurate predictions.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"1126-1138"},"PeriodicalIF":3.2,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10591494","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-09DOI: 10.1109/TBC.2024.3405313
Fei Qi;Lei Liu;Weiliang Xie
This paper studies the realization of wireless video transmission by leveraging 5G mixed mode with multimedia broadcast multicast services (MBMS). In particular, it investigates a number of key elements, such as physical layer modeling and precoding strategies, for MBMS implementation with large-scale multi-input multi-output (MIMO). A novel hybrid 5G mixed mode system is proposed to seamlessly integrate unicast and multicast transmissions, wherein system architecture, user grouping strategies, interference mitigation techniques, and optimized multicast beamforming approach are comprehensively elucidated. The performance of our proposed system is assessed through comprehensive simulations and analysis. The results indicate significant improvements in coding and spectral efficiencies while combining MIMO with layer division multiplexing (LDM).
{"title":"Hybrid Unicast/Multicast Massive MIMO Precoding for 5G Mixed Mode","authors":"Fei Qi;Lei Liu;Weiliang Xie","doi":"10.1109/TBC.2024.3405313","DOIUrl":"10.1109/TBC.2024.3405313","url":null,"abstract":"This paper studies the realization of wireless video transmission by leveraging 5G mixed mode with multimedia broadcast multicast services (MBMS). In particular, it investigates a number of key elements, such as physical layer modeling and precoding strategies, for MBMS implementation with large-scale multi-input multi-output (MIMO). A novel hybrid 5G mixed mode system is proposed to seamlessly integrate unicast and multicast transmissions, wherein system architecture, user grouping strategies, interference mitigation techniques, and optimized multicast beamforming approach are comprehensively elucidated. The performance of our proposed system is assessed through comprehensive simulations and analysis. The results indicate significant improvements in coding and spectral efficiencies while combining MIMO with layer division multiplexing (LDM).","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"1044-1051"},"PeriodicalIF":3.2,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573015","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}
HTTP live streaming delivers dynamically video content with varying bitrates to accommodate the dynamic real-time bandwidth fluctuations while considering diverse user preferences and device capabilities. Existing flow control solutions do not provide support for new features such as multi-source content transmission. In this paper, we propose a distributed multi-source rate control optimization algorithm (DMRCA) that maximizes the overall network bandwidth utility and improves viewer Quality of Experience (QoE). First, we model the rate control problem as a dual-optimized multi-source and multi-rate problem. Then, we decompose the problem into sub-problems of source rate selection and user rate adaptation and we prove that solving the original problem is equivalent to solving these two sub-problems. Furthermore, we propose DMRCA as a fully distributed algorithm to solve these sub-problems and derive an optimal solution and we discuss DMRCA’s complexity and convergence. Finally, through a series of simulation tests, we demonstrate the superiority of our proposed algorithm compared to alternative state-of-the-art solutions.
{"title":"A Novel Distributed Multi-Source Optimal Rate Control Solution for HTTP Live Video Streaming","authors":"Shujie Yang;Chuxing Fang;Lujie Zhong;Mu Wang;Zan Zhou;Han Xiao;Hao Hao;Changqiao Xu;Gabriel-Miro Muntean","doi":"10.1109/TBC.2024.3391051","DOIUrl":"10.1109/TBC.2024.3391051","url":null,"abstract":"HTTP live streaming delivers dynamically video content with varying bitrates to accommodate the dynamic real-time bandwidth fluctuations while considering diverse user preferences and device capabilities. Existing flow control solutions do not provide support for new features such as multi-source content transmission. In this paper, we propose a distributed multi-source rate control optimization algorithm (DMRCA) that maximizes the overall network bandwidth utility and improves viewer Quality of Experience (QoE). First, we model the rate control problem as a dual-optimized multi-source and multi-rate problem. Then, we decompose the problem into sub-problems of source rate selection and user rate adaptation and we prove that solving the original problem is equivalent to solving these two sub-problems. Furthermore, we propose DMRCA as a fully distributed algorithm to solve these sub-problems and derive an optimal solution and we discuss DMRCA’s complexity and convergence. Finally, through a series of simulation tests, we demonstrate the superiority of our proposed algorithm compared to alternative state-of-the-art solutions.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"792-807"},"PeriodicalIF":3.2,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10589341","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Linear discriminant analysis (LDA) is a well-known feature-extraction technique for data analytic and pattern classification. As the dimensionality of multimedia data has increased in this big era, it is often to characterize data by tensors. Over the past two decades, researchers have thus explored to extend LDA to the general tensor space, especially in two common ways: LDA of tensors using tensor decomposition methods (by conversion of tensors to matrices) and LDA of tensors built upon the T-product. However, both of the aforementioned approaches have restrictions thereby. A critical problem about how to carry out LDA of arbitrary scatter tensors based on the Einstein product still remains unsolved by the existing methods. Therefore, we propose a novel tensor LDA (a.k.a. TLDA) approach, which can carry out the LDA of arbitrary-dimensional scatter-tensors without any need of tensor decomposition. Besides, for reducing the computation time, we also design a parallel paradigm to execute our proposed TLDA in this work. Numerical experiments conducted over real multimedia data demonstrate the efficacy of our proposed new TLDA in terms of classification accuracy. Moreover, the comparison of the classification accuracies, computational-complexities, and memory-complexities of our proposed novel TLDA scheme and other existing tensor-based LDA methods is made. By leveraging TLDA for high-dimensional feature extraction, segmentation, and user-item interaction data processing, future multimedia recommendation systems can facilitate more accurate, engaging, and satisfactory user experience over the Internet.
{"title":"Multimedia Classification via Tensor Linear Discriminant Analysis","authors":"Shih-Yu Chang;Hsiao-Chun Wu;Kun Yan;Scott Chih-Hao Huang;Yiyan Wu","doi":"10.1109/TBC.2024.3417342","DOIUrl":"10.1109/TBC.2024.3417342","url":null,"abstract":"Linear discriminant analysis (LDA) is a well-known feature-extraction technique for data analytic and pattern classification. As the dimensionality of multimedia data has increased in this big era, it is often to characterize data by tensors. Over the past two decades, researchers have thus explored to extend LDA to the general tensor space, especially in two common ways: LDA of tensors using tensor decomposition methods (by conversion of tensors to matrices) and LDA of tensors built upon the T-product. However, both of the aforementioned approaches have restrictions thereby. A critical problem about how to carry out LDA of arbitrary scatter tensors based on the Einstein product still remains unsolved by the existing methods. Therefore, we propose a novel tensor LDA (a.k.a. TLDA) approach, which can carry out the LDA of arbitrary-dimensional scatter-tensors without any need of tensor decomposition. Besides, for reducing the computation time, we also design a parallel paradigm to execute our proposed TLDA in this work. Numerical experiments conducted over real multimedia data demonstrate the efficacy of our proposed new TLDA in terms of classification accuracy. Moreover, the comparison of the classification accuracies, computational-complexities, and memory-complexities of our proposed novel TLDA scheme and other existing tensor-based LDA methods is made. By leveraging TLDA for high-dimensional feature extraction, segmentation, and user-item interaction data processing, future multimedia recommendation systems can facilitate more accurate, engaging, and satisfactory user experience over the Internet.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 4","pages":"1139-1152"},"PeriodicalIF":3.2,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141573017","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}
With the escalating prevalence of datacasting, live streaming and high-quality video consumption on mobile devices, there arises an increasing demand for a cost-effective and reliable approach to transmit large volumes of such content to extensive audiences. While broadband mobile networks can increase capacity through denser base stations and higher frequencies, the linear pace of facility development makes it difficult to match the non-linear growth of the service throughput. Terrestrial Broadcast has proven itself to be significantly more efficient in transmitting popular video streams to mobile devices over a large area. However, due to its downlink-only nature, it falls short of delivering consistently reliable services. Hence, the convergence of terrestrial broadcast and broadband mobile networks has resurfaced as a pertinent topic for consideration. In this paper, terrestrial broadcast is adopted as the main pipe to transmit streaming services to mobile phones, with a 5th generation mobile communications (5G) new radio (NR) mobile carrier employed to provide complementary packet loss retransmission service, ensuring a seamless service experience. First, a cross-standard packet retransmission (CPR) scheme is proposed based on 5G broadcast and 5G NR Systems. Corresponding protocols and schemes are introduced, and a prototype system is realized. CPR is able to support delay-insensitive datacasting services very well, yet its higher layer convergence poses challenges for supporting delay-sensitive real-time services. To address this, a MAC-layer homogeneous packet retransmission (HPR) scheme is proposed. The basic principle is to utilize the carrier aggregation mechanism of 5G, modifying the protocols to enable one carrier to simulate broadcast while maintaining unicast in another carrier. In HPR, packet retransmission can be done at the MAC layer, reducing the retransmission delay to within 5 microseconds. Simulation and trial results are presented based on the proposed schemes.
随着数据传输、流媒体直播和高质量视频消费在移动设备上的日益普及，人们越来越需要一种具有成本效益且可靠的方法来向广大受众传输大量此类内容。虽然宽带移动网络可以通过更密集的基站和更高的频率来提高容量，但设施的线性发展速度很难与服务吞吐量的非线性增长相匹配。事实证明，地面广播在向大范围移动设备传输流行视频流方面效率更高。然而，由于其仅具有下行链路的特性，它无法提供持续可靠的服务。因此，地面广播与宽带移动网络的融合再次成为需要考虑的相关话题。本文采用地面广播作为向手机传输流媒体服务的主要管道，并利用第五代移动通信（5G）新无线电（NR）移动载波提供互补的丢包重传服务，确保无缝的服务体验。首先，提出了一种基于 5G 广播和 5G NR 系统的跨标准数据包重传（CPR）方案。介绍了相应的协议和方案，并实现了一个原型系统。CPR 能够很好地支持对延迟不敏感的数据广播服务，但其高层融合对支持对延迟敏感的实时服务提出了挑战。为解决这一问题，提出了一种 MAC 层同质数据包重传（HPR）方案。其基本原理是利用 5G 的载波聚合机制，修改协议使一个载波能够模拟广播，同时在另一个载波中保持单播。在 HPR 中，数据包重传可在 MAC 层完成，从而将重传延迟减少到 5 微秒以内。本文介绍了基于所提方案的仿真和试验结果。
{"title":"Packet Retransmission Schemes and Trials for Broadcast Services in Mobile Scenarios","authors":"Yin Xu;Hao Ju;Zigang Fu;Xin Lin;Tianyao Ma;Dazhi He;Yang Chen;Dajun Zhang;Ke Wang;Wenjun Zhang;Yiyan Wu","doi":"10.1109/TBC.2024.3410706","DOIUrl":"10.1109/TBC.2024.3410706","url":null,"abstract":"With the escalating prevalence of datacasting, live streaming and high-quality video consumption on mobile devices, there arises an increasing demand for a cost-effective and reliable approach to transmit large volumes of such content to extensive audiences. While broadband mobile networks can increase capacity through denser base stations and higher frequencies, the linear pace of facility development makes it difficult to match the non-linear growth of the service throughput. Terrestrial Broadcast has proven itself to be significantly more efficient in transmitting popular video streams to mobile devices over a large area. However, due to its downlink-only nature, it falls short of delivering consistently reliable services. Hence, the convergence of terrestrial broadcast and broadband mobile networks has resurfaced as a pertinent topic for consideration. In this paper, terrestrial broadcast is adopted as the main pipe to transmit streaming services to mobile phones, with a 5th generation mobile communications (5G) new radio (NR) mobile carrier employed to provide complementary packet loss retransmission service, ensuring a seamless service experience. First, a cross-standard packet retransmission (CPR) scheme is proposed based on 5G broadcast and 5G NR Systems. Corresponding protocols and schemes are introduced, and a prototype system is realized. CPR is able to support delay-insensitive datacasting services very well, yet its higher layer convergence poses challenges for supporting delay-sensitive real-time services. To address this, a MAC-layer homogeneous packet retransmission (HPR) scheme is proposed. The basic principle is to utilize the carrier aggregation mechanism of 5G, modifying the protocols to enable one carrier to simulate broadcast while maintaining unicast in another carrier. In HPR, packet retransmission can be done at the MAC layer, reducing the retransmission delay to within 5 microseconds. Simulation and trial results are presented based on the proposed schemes.","PeriodicalId":13159,"journal":{"name":"IEEE Transactions on Broadcasting","volume":"70 3","pages":"1113-1125"},"PeriodicalIF":3.2,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528458","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}
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