Showing papers in "IEEE Transactions on Broadcasting in 2019"

High-Level Multiple-UAV Cinematography Tools for Covering Outdoor Events

Abstract: Camera-equipped unmanned aerial vehicles (UAVs), or “drones,” are a recent addition to standard audiovisual shooting technologies. As drone cinematography is expected to further revolutionize media production, this paper presents an overview of the state-of-the-art in this area, along with a brief review of current commercial UAV technologies and legal restrictions on their deployment. A novel taxonomy of UAV cinematography visual building blocks, in the context of filming outdoor events where targets (e.g., athletes) must be actively followed, is additionally proposed. Such a taxonomy is necessary for progress in intelligent/autonomous UAV shooting, which has the potential of addressing current technology challenges. Subsequently, the concepts and advantages inherent in multiple-UAV cinematography are introduced. The core of multiple-UAV cinematography consists in identifying different combinations of multiple single-UAV camera motion types, assembled in meaningful sequences. Finally, based on the defined UAV/camera motion types, tools for managing a partially autonomous, multiple-UAV fleet from the director’s point of view are presented. Although the overall focus is on cinematic coverage of sports events, the majority of our contributions also apply in different scenarios, such as movies/TV production, newsgathering, or advertising.

5G New Radio for Terrestrial Broadcast: A Forward-Looking Approach for NR-MBMS

Abstract: 3GPP LTE eMBMS release (Rel-) 14, also referred to as further evolved multimedia broadcast multicast service (FeMBMS) or enhanced TV (EnTV), is the first mobile broadband technology standard to incorporate a transmission mode designed to deliver terrestrial broadcast services from conventional high power high tower (HPHT) broadcast infrastructure. With respect to the physical layer, the main improvements in FeMBMS are the support of larger inter-site distance for single frequency networks (SFNs) and the ability to allocate 100% of a carrier’s resources to the broadcast payload, with self-contained signaling in the downlink. From the system architecture perspective, a receive-only mode enables free-to-air (FTA) reception with no need for an uplink or SIM card, thus receiving content without user equipment registration with a network. These functionalities are only available in the LTE advanced pro specifications as 5G new radio (NR), standardized in 3GPP from Rel-15, has so far focused entirely on unicast. This paper outlines a physical layer design for NR-MBMS, a system derived, with minor modifications, from the 5G-NR specifications, and suitable for the transmission of linear TV and radio services in either single-cell or SFN operation. This paper evaluates the NR-MBMS proposition and compares it to LTE-based FeMBMS in terms of flexibility, performance, capacity, and coverage.

An Edge-to-Cloud Virtualized Multimedia Service Platform for 5G Networks

Abstract: The focus of research into 5G networks to date has been largely on the required advances in network architectures, technologies, and infrastructures. Less effort has been put on the applications and services that will make use of and exploit the flexibility of 5G networks built upon the concept of software-defined networking (SDN) and network function virtualization (NFV). Media-based applications are amongst the most demanding services, requiring large bandwidths for high audio–visual quality, low-latency for interactivity, and sufficient infrastructure resources to deliver the computational power for running the media applications in the networked cloud. This paper presents a novel service virtualization platform (SVP), called 5G-MEDIA SVP, which leverages the principles of NFV and SDN to facilitate the development, deployment, and operation of media services on 5G networks. The platform offers an advanced cognitive management environment for the provisioning of network services (NSs) and media-related applications, which directly link their lifecycle management with user experience as well as optimization of infrastructure resource utilization. Another innovation of 5G-MEDIA SVP is the integration of serverless computing with media intensive applications in 5G networks, increasing cost effectiveness of operation and simplifying development and deployment time. The proposed SVP is being validated against three media use cases: 1) immersive virtual reality 3-D gaming application; 2) remote production of broadcast content incorporating user generated contents; and 3) dynamically adaptive content distribution networks for the intelligent distribution of ultrahigh definition content. The preliminary results of the 5G-MEDIA SVP platform evaluation are compared against current practice and show that the proposed platform provides enhanced functionality for the operators and infrastructure owners, while ensuring better NS performance to service providers and end users.

MIMO Applications for Multibeam Satellites

Abstract: High throughput satellites employing multibeam antennas and full frequency reuse for broadband satellite services are considered in this paper. Such architectures offer, for example, a cost-effective solution to optimize data delivery and extend the coverage areas in future 5G networks. We propose the application of the multiple-input-multiple-output (MIMO) technology in both the feeder link and the multiuser downlink. Spatial multiplexing of different data streams is performed in a common feeder beam. In the user links, MIMO with multiple beams is exploited to simultaneously serve different users in the same frequency channel. Under particular design constraints, effective spatial separation of the multiple user signals is possible. To mitigate the inter-stream interference in the MIMO feeder link as well as the multiuser downlink, precoding of the transmit signals is applied. Simulation results illustrate the performance gains in terms of sum throughput.

Reduced Reference Quality Assessment of Light Field Images

Abstract: Immersive media, such as free view point video and 360° video, are expected to be dominant as broadcasting services. The light field (LF) imaging is being considered as a next generation imaging technology offering the possibility to provide new services, including six degree-of-freedom video. The drawback of this technology is in the size of the generated content thus requiring novel compression systems and the design of ad-hoc methodologies for evaluating the perceived quality. In this paper, the relation between depth map quality and overall quality of LF image is studied. Next, a reduced reference quality assessment metric for LF images is presented. To predict the quality of distorted LF images, the measure of distortion in the depth map is exploited. To test and validate the proposed framework, a subjective experiment has been performed, and a LF image quality dataset has been created. The dataset is also used for evaluating the performance of state-of-the-art quality metrics, when applied to LF images. The achieved results evidence that the estimated quality score by the proposed framework has a significant correlation with subjective quality rating. Consequently, reference data can be delivered to the clients thus allowing the local estimation of the perceived quality of service.

QoE-Assured Live Streaming via Satellite Backhaul in 5G Networks

Abstract: Satellite communication has recently been included as one of the key enabling technologies for 5G backhauling, especially for the delivery of bandwidth-demanding enhanced mobile broadband (eMBB) applications in 5G. In this paper, we present a 5G-oriented network architecture that is based on satellite communications and multi-access edge computing to support eMBB applications, which is investigated in the EU 5GPPP phase-2 satellite and terrestrial network for 5G project. We specifically focus on using the proposed architecture to assure quality-of-experience (QoE) of HTTP-based live streaming users by leveraging satellite links, where the main strategy is to realize transient holding and localization of HTTP-based (e.g., MPEG-DASH or HTTP live streaming) video segments at 5G mobile edge while taking into account the characteristics of satellite backhaul link. For the very first time in the literature, we carried out experiments and systematically evaluated the performance of live 4K video streaming over a 5G core network supported by a live geostationary satellite backhaul, which validates its capability of assuring live streaming users’ QoE under challenging satellite network scenarios.

Enable Advanced QoS-Aware Network Slicing in 5G Networks for Slice-Based Media Use Cases

Abstract: Media use cases for emergency services require mission-critical levels of reliability for the delivery of media-rich services, such as video streaming. With the upcoming deployment of the fifth generation (5G) networks, a wide variety of applications and services with heterogeneous performance requirements are expected to be supported, and any migration of mission-critical services to 5G networks presents significant challenges in the quality of service (QoS), for emergency service operators. This paper presents a novel SliceNet framework, based on advanced and customizable network slicing to address some of the highlighted challenges in migrating eHealth telemedicine services to 5G networks. An overview of the framework outlines the technical approaches in beyond the state-of-the-art network slicing. Subsequently, this paper emphasizes the design and prototyping of a media-centric eHealth use case, focusing on a set of innovative enablers toward achieving end-to-end QoS-aware network slicing capabilities, required by this demanding use case. Experimental results empirically validate the prototyped enablers and demonstrate the applicability of the proposed framework in such media-rich use cases.

Implementation Methodologies of Deep Learning-Based Signal Detection for Conventional MIMO Transmitters

Abstract: In this paper, simple methodologies of deep learning application to conventional multiple-input multiple-output (MIMO) communication systems are presented. The deep learning technologies with deep neural network (DNN) structure, emerging technologies in various engineering areas, have been actively investigated in the field of communication engineering as well. In the physical layer of conventional communication systems, there are practical challenges of application of DNN: calculating complex number in DNN and designing proper DNN structure for a specific communication system model. This paper proposes and verifies simple solutions for the difficulty. First, we apply a basic DNN structure for signal detection of one-tap MIMO channel. Second, convolutional neural network (CNN) and recurrent neural network (RNN) structures are presented for MIMO system with multipath fading channel. Our DNN structure for one-tap MIMO channel can achieve the optimal maximum likelihood detection performance, and furthermore, our CNN and RNN structures for multipath fading channel can detect the transmitted signal properly.

High-Capacity 5G Fronthaul Networks Based on Optical Space Division Multiplexing

Abstract: The introduction of 5G mobile networks, bringing multi-Gbit/s user data rates and reduced latency, opens new opportunities for media generation, transport and distribution, as well as for new immersive media applications. The expected use of millimeter-wave carriers and the strong network densification resulting from a much reduced cell size—which enable the expected performance of 5G—pose major challenges to the fronthaul network. Space division multiplexing (SDM) in the optical domain has been suggested for ultra-high capacity fronthaul networks that naturally support different classes of fronthaul traffic and further enable the use of analog radio-over-fiber and advanced technologies, such as optical beamforming. This paper discusses the introduction of SDM with multi-core fibers in the fronthaul network as suggested by the blueSPACE project, regarding both digitized and analog radio-over-fiber fronthaul transport as well as the introduction of optical beamforming for high-capacity millimeter-wave radio access. Analog and digitized radio-over-fiber are discussed in a scenario featuring parallel fronthaul for different radio access technologies, showcasing their differences and potential when combined with SDM.

Modeling Mobile Edge Computing Deployments for Low Latency Multimedia Services

Abstract: Multi-access edge computing (MEC) technologies bring important improvements in terms of network bandwidth, latency, and use of context information and critical for services like multimedia streaming, augmented, and virtual reality. In future deployments, operators will need to decide how many MEC points of presence (PoPs) are needed and where to deploy them, also considering the number of base stations needed to support the expected traffic. This paper presents an application of inhomogeneous Poisson point processes with hard-core repulsion to model feasible MEC infrastructure deployments. With the presented methodology a mobile network operator knows where to locate the MEC PoPs and associated base stations to support a given set of services. We evaluate our model with simulations in realistic scenarios, namely Madrid City Center, an industrial area and a rural area.

Optimal Bit Allocation at Frame Level for Rate Control in HEVC

Abstract: Rate control plays an important role in video coding systems, which makes the output bitrate of a video encoder equal to the target bitrate while minimizing the distortion of the compressed video. However, most of the existing rate control schemes achieve accurate bitrate control at the loss of rate-distortion (R-D) performance. This paper proposes an effective frame level bit allocation method to improve the R-D performance whilst maintaining the high accuracy of the bitrate control in HEVC. First, an improved R-D model is presented at the frame level, which, by making use of the information of encoded frames more completely, achieves higher accuracy with lower bitrate mismatch rate. Second, a group of picture (GOP) level bit allocation approach is introduced with the consideration of the temporal R-D dependency among different GOPs, which can further enhance the R-D performance. Finally, to achieve optimal R-D performance globally, a formulation for optimal frame level bit allocation is developed with a GOP level Lagrange multiplier introduced, which takes into account the coding effects of a current frame on the other frames within the same GOP. A scheme of recursive Taylor expansion is employed to find the GOP level Lagrange multiplier. Experimental results demonstrate that the proposed method can achieve considerable R-D performance improvement and accurate bitrate control. Specifically, our method shows 3.9% and 3.8% BD-rate saving in average compared against the HEVC reference software HM16.7 with rate control in the low-delay B and low-delay P coding structures, respectively.

Fast HEVC Intra Mode Decision Based on RDO Cost Prediction

Abstract: High efficiency video coding (HEVC) increases the number of intra coding modes to 35 to provide higher coding efficiency than previous video coding standards. This results in an increased encoder complexity, since there are more modes to be processed by the high resource-demanding rate-distortion optimization (RDO). In this paper, we propose a novel method to reduce the HEVC intra mode decision computational complexity and encoding time. This method is based on the prediction of the RDO cost of intra modes from a low-complexity sum of absolute transformed differences-based cost. By predicting the RDO cost, we are able to exclude non-promising modes from further processing and thereby save substantial computations. Also, a gradient-based method, using the Prewitt operator, is proposed to eliminate the non-relevant directional modes from the list of candidates. For even more complexity reduction, a mode classification is proposed to adaptively reduce chroma intra modes based on block texture. Experimental results show that we achieve a 47.3% encoding time reduction on average with a negligible quality loss of 0.062 dB for the Bjontegaard delta peak signal-to-noise ratio when we compare our method to the HEVC test model 15.0.

Multicast and Broadcast Enablers for High-Performing Cellular V2X Systems

Abstract: This paper focuses on capabilities enabled by 5G connectivity in the cooperative, connected and autonomous cars, and elaborates on two technical enablers. One of the technical enablers consists of a beamformed broadcast/multicast technology that builds on adaptive and robust beam management techniques at the air interface. The other proposed technical component aims to improve the end-to-end architectural design of 5G networks to enable efficient broadcast and multicast transmissions for vehicle-to-anything services. Finally, the key results of multicast and broadcast technical components are described and ongoing and future areas of work and research are detailed.

Performance Analysis of All Modulation and Code Combinations in ATSC 3.0 Physical Layer Protocol

Abstract: This paper presents the advanced television systems committee (ATSC) 3.0 physical layer system performances with different modulation and channel coding combinations. Numerous computer simulations, laboratory tests, and field trials are conducted under additive white Gaussian noise, RC20, and RL20 channels. Analysis of the results shows that the measured values in laboratory and field are less than 1 dB away from computer simulation results. This confirms that the ATSC 3.0 physical layer is capable of providing services ranging from ultra-robust reception (negative SNR operation with QPSK and 2/15 low density parity check (LDPC) code) to very high-throughput (over 50 Mb/s with 4096-non-uniform constellation and 13/15 LDPC code) in real field environments.

A Hybrid Unicast-Multicast Network Selection for Video Deliveries in Dense Heterogeneous Network Environments

Abstract: Resource management in emerging dense heterogeneous network environments (DenseNets) is a challenging issue. The employment of multicast transmissions in this scenario has potential to address the problems. On one hand, the large number of smart user mobile devices and user expectations for high-quality rich media services has determined a growing demand for network resources; in DenseNets, mobile users have to make the choice in terms of the network to connect to, in order to balance energy saving and delivery performance. On the other hand, the proliferation of user accesses to the existing and future network infrastructure will bring along with it the operators need for optimizing the radio resource usage. This paper proposes a hybrid unicast-multicast utility-based network selection algorithm (HUMANS), which offers the additional option of selecting multicast transmissions in the network selection process during video delivery. By serving users with good channel conditions via unicast transmissions and users with poor channel quality conditions via multicast, HUMANS allows outperforming other solutions in terms of outage percentage and average quality of transmission, in both low- and high-density scenarios. Most importantly, at the same time it guarantees operators a more efficient resource utilization.

Deploying a Novel 5G-Enabled Architecture on City Infrastructure for Ultra-High Definition and Immersive Media Production and Broadcasting

Abstract: With the massive growth of cutting-edge media services, such as ultra-high definition video and immersive media (i.e., virtual and augmented reality), demand for large investments in a scalable, ubiquitous, and robust communication infrastructure and services increases enormously. The H2020 5GCity project aims to provide a solution for such issues by designing, developing, and deploying a sliceable, distributed cloud/edge and radio platform with neutral hosting capability to support the sharing between information technology infrastructure owners and media service providers (i.e., vertical media actors). In this paper, we initially introduce the essential benefits of the 5GCity technology and neutral host model to facilitate the rise of highly demanding media use cases (UCs) and its implication on how service providers typically operate (in terms of business model). Then, we show how the 5GCity architecture and infrastructure, in light of certain key performance indicators, address this demand through three media UCs (namely related to “video acquisition and production at the edge,” “immersive services,” and “mobile production and transmission”) and we explain how they are implemented and deployed in real citywide pilots (in Bristol, Lucca, and Barcelona) to demonstrate the benefits for infrastructure owners and media service providers.

Prediction of Digital Terrestrial Television Coverage Using Machine Learning Regression

Abstract: Appropriate coverage prediction is a fundamental task for an operator during the dimensioning process and planning of a digital terrestrial television (DTT) system because it allows offering a satisfactory quality of service to end users. Accordingly, several prediction methods based on propagation path loss estimation and traditional statistical models have been proposed. However, the choice of model depends on many factors, such as the presence of obstacles (buildings, trees, and so on) and propagation paths. This fact leads to increasing the error gap between the predicted and real value, which varies from one propagation model to the next. Therefore, novel techniques are required to achieve a high accuracy in the prediction of the signal strength based on few local measurements over the zone of interest. A machine learning regression algorithm is a novel approach that improves the accuracy of DTT coverage prediction regardless of the aforementioned constraints. To this end, we propose an approach based on clustering and machine regression algorithms, such as random forest regression, AdaBoost regression, and ${K}$ -nearest neighbors regression, where we choose the best algorithm for our approach. We use real measurements in terms of electric field strength corresponding to eight DTT channels operating in the city of Quito, Ecuador. Furthermore, we display the coverage results in Google Maps. We perform extensively simulation analysis based on the tenfold cross validation method to evaluate the performance of the machine learning regressor algorithms and compare the results in three error metrics with support vector regression, lasso regression, multilayer perceptron regression, and ordinary kriging technique. Satisfactorily, the results using random forest regression depict a considerable improvement in the accuracy of coverage prediction under a low computational load.

Physical Layer Performance Evaluation of LTE-Advanced Pro Broadcast and ATSC 3.0 Systems

Abstract: This paper provides a detailed performance analysis of the physical layer of two state-of-the-art point-to-multipoint (PTM) technologies: evolved Multimedia Broadcast Multicast Service (eMBMS) and Advanced Television Systems Committee - Third Generation (ATSC 3.0). The performance of these technologies is evaluated and compared using link-level simulations, considering relevant identified scenarios. A selection of Key Performance Indicators for the International Mobile Telecommunications 2020 (IMT-2020) evaluation process has been considered. Representative use cases are also aligned to the test environments as defined in the IMT-2020 evaluation guidelines. It is observed that ATSC 3.0 outperforms both eMBMS solutions, i.e., MBMS over Single Frequency Networks (MBSFN) and Single-Cell PTM (SC-PTM) in terms of spectral efficiency, peak data rate and mobility, among others. This performance evaluation serves as a benchmark for comparison with a potential 5G PTM solution.

Precoding for Flexible High Throughput Satellites: Hot-Spot Scenario

Abstract: Motivated by the need of resource flexibility in high throughput satellite systems, a hot-spot cell configuration is introduced and analyzed in terms of throughput performance. Cell partitioning and user scheduling are also addressed in conjunction with the application of different types of linear precoding algorithms. The analysis is based on a real satellite system design and uses a system simulation approach, encompassing the random user distribution inside the hot-spot cell area. Results provide guidelines on the design of the hot-spot cell partitioning and show the achievable gains of precoding with respect to conventional, non precoded, transmission.

Smart Television Services Using NFV/SDN Network Management

Abstract: Integrating joint network function virtualization (NFV) and software-defined networks (SDNs) with digital televisions (TVs) into home environments, has the potential to provide smart TV services to users, and improve their quality of experience (QoE). In this regard, this paper focuses on one of the next generation services so-called follow me service (FMS). FMS is a service offered by 5gNB to user equipments (UEs) in indoor environments (e.g., home), it enables its clients to use their smart phones to select media content from content servers, then cast it on the nearest TV set (e.g., living room) and continue watching on the next TV set (e.g., kitchen) while moving around the indoor coverage area. FMS can be provisioned by utilizing UEs geolocation information and robust mechanisms for switching between multiple 5G radio access technologies (RATs), based on the intelligence of the SDN/NFV intelligent home IP gateway of the Internet of Radio Light (IoRL) project paradigm. In view that the actual IoRL system is at its early development stage, we step forward by using Mininet platform to integrate SDN/NFV virtualization into 5G multi-RAT scenario and provide performance monitoring with measurements for the identified service. Simulation results show the effectiveness of our proposal under various use case scenarios by means of minimizing the packet loss rate and improving QoE of the home users.

Comparison of Low-Density Parity-Check Codes in ATSC 3.0 and 5G Standards

Abstract: Recently, low-density parity-check (LDPC) codes have been adopted in Advanced Television Systems Committee 3.0 and 3rd Generation Partnership Project 5G standards. In this paper, we present their structures in detail. They are delicately designed, based on the structures of quasi-cyclic LDPC codes and multi-edge type LDPC codes. The differences in their base matrices and parity-check matrices used in both standards are highlighted from the viewpoint of the distinction between broadcasting and cellular communication systems. Numerical results show that they are very competitive in their respective areas.

Full-Reference Image Quality Assessment by Combining Features in Spatial and Frequency Domains

Abstract: Objective image quality assessment employs mathematical and computational theory to objectively assess the quality of output images based on the human visual system (HVS). In this paper, a novel approach based on multifeature extraction in the spatial and frequency domains is proposed. We combine the gradient magnitude and phase congruency maps to generate a local structure (LS) map, which can perceive local structural distortions. The LS matches well with HVS and highlights differences with details. For complex visual information, such as texture and contrast sensitivity, we deploy the log-Gabor filter, and spatial frequency, respectively, to effectively capture their variations. Moreover, we employ the random forest (RF) to overcome the limitations of existing pooling methods. Compared with support vector regression, RF can obtain better prediction results. Extensive experimental results on the five benchmark databases indicate that the proposed method precedes all the state-of-the-art image quality assessment metrics in terms of prediction accuracy. In addition, the proposed method is in compliance with the subjective evaluations.

Using Layered-Division-Multiplexing to Deliver Multi-Layer Mobile Services in ATSC 3.0

Abstract: Layered-division-multiplexing (LDM) is a physical-layer non-orthogonal multiplexing technology, which has been accepted as a baseline technology by the next generation terrestrial digital TV (DTV) broadcasting standard, advanced television systems committee (ATSC) 3.0. The typical scenario for an LDM-based ATSC 3.0 system is to deliver a robust high-definition TV (HDTV) service in the higher-power core-layer (CL) for mobile reception and a 4k ultra high definition TV or multiple enhanced full HDTV (1080p) services in the lower-power enhanced layer (EL) for fixed reception. So far, the capability of using the EL of an LDM system to deliver mobile services has not been investigated and well-understood. In this paper, we investigate the potential of using both layers of an LDM-based ATSC 3.0 system to delivery multiple mobile services that target different types of mobile devices. The mobile performance of the LDM system is then compared to its time-division-multiplexing and frequency-division-multiplexing counterparts, which will reveal the application scenarios for which using LDM would provide performance advantages. In addition, the availability of high-throughput fixed services carried in the LDM-EL to mobile receivers is also investigated by computer simulations. An advanced detection algorithm is proposed based on the LDM-CL inter-carrier-interference cancellation, which is shown to provide significantly better mobile performance. Finally, we show by simulation that using receive antenna diversity combining techniques can significantly improve the service detection performance of LDM mobile receivers, especially for services carried in the LDM EL.

Prototype Filter Design to Minimize Stopband Energy With Constraint on Channel Estimation Performance for OQAM/FBMC Systems

Abstract: In this paper, we propose a prototype filter design method to minimize the stopband energy of the filter with guaranteed time-domain channel estimation performance for offset quadrature amplitude modulation based filter bank multicarrier (OQAM/FBMC) systems. First, we analytically derive the total mean squared error (MSE) of estimated channel impulse response with two channel estimation schemes, i.e., the linear minimum mean square error and weighted least square, where the impact of intrinsic interference from data symbols is taken into account. Then, we formulate an optimization problem of filter coefficients to minimize the stopband energy with constraint on the total MSE. By exploiting the Heisenberg–Gabor uncertainty principle, the original optimization problem is transformed into an equality-constrained one. Finally, we employ the Lagrange multiplier method to solve the transformed problem and obtain the filter coefficients with the Newton’s method. Simulation results demonstrate that the proposed filter outperforms the isotropic orthogonal transform algorithm and extended Gaussian function filters considering both the MSE and stopband energy performances.

Data-Driven Rate Control for Rate-Distortion Optimization in HEVC Based on Simplified Effective Initial QP Learning

Abstract: Different from the conventional calculative methods, a learning-based initial quantization parameter (LIQP) method is proposed in this paper to improve rate control of high efficiency video coding (H265) First, the framework for initial quantization parameter (QP) learning is proposed, where a novel equivalent approach to build the benchmark labels is proposed using the single rate-distortion (R-D) pair in each initial QP testing With the criterion of maximizing the prediction accuracy of initial QPs, features and parameters of the learning model are refined Instead of the traditionally used target bits per pixel (bpp) for intraframe, the target bpp for all remaining frames is proposed to avoid the empirical setting on intracoding bits, and thus the related inaccuracy can be prevented We clearly present the motivations of the proposed LIQP method, as well as the reasons for the extracted features and model parameters The proposed LIQP method outperforms the latest HM-1614 by achieving significant gains on R-D performance (−1548% BD-BR and 0782 dB BD-PSNR gains), quality smoothness (1581 dB versus 2598 dB), and more stable buffer occupancy control, with similar high bit rate accuracy (9984% versus 9987%), and can also work well for scene change cases

IEEE Transactions on Broadcasting Special Issue on: 5G for Broadband Multimedia Systems and Broadcasting

Abstract: The upcoming fifth-generation ( 5G ) of wireless communications technologies is expected to revolutionize society digital transformation thanks to its unprecedented wireless performance capabilities, providing speeds of several Gbps, very low latencies well below 5 ms, ultra-reliable transmissions with up to 99.999% success probability, while being able to handle a huge number of devices simultaneously connected to the network. The first version of the 3GPP specification (i.e., Release 15) has been recently completed and many 5G trials are under plan or carrying out worldwide, with the first commercial deployments happening in 2019.

Bitrate-Based No-Reference Video Quality Assessment Combining the Visual Perception of Video Contents

Abstract: In video communication, the quality of video is mainly determined by bitrate in general. Moreover, the effect of video contents and their visual perception on video quality assessment (VQA) is often overlooked. However, in fact, for different videos, although the bitrates are the same, their VQA scores are still significantly different. Hence, it is assumed that the bitrate, video contents, and human visual characteristics mainly affect the VQA. Based on the above three aspects, in this paper, we designed a bitrate-based no-reference (NR) VQA metric combining the visual perception of video contents, namely, BRVPVC. In this metric, first an initial VQA model was proposed by only considering the bitrate alone. Then, the visual perception model for video contents was designed based on the texture complexity and local contrast of image, temporal information of video, and their visual perception features. Finally, two models were synthesized by adding certain weight coefficients into an overall VQA metric, namely, BRVPVC. Furthermore, ten reference videos and 150 distorted videos in the LIVE video database were used to test the metric. Moreover, based on the results of evaluating the videos in LIVE, VQEG, IRCCyN, EPFL-PoliMI, IVP, CSIQ, and Lisbon databases, the performance of BRVPVC is respectively compared with that of six full-reference (FR) metrics and ten NR VQA metrics. The results show that our VQA metric has a higher accuracy than six common FR VQA metrics and eight NR VQA metrics, and it is close to other two NR VQA metrics in accuracy. The corresponding values of Pearson linear correlation coefficient and Spearman rank order correlation coefficient reached 0.8547 and 0.8260, respectively. In addition, the computational complexity of proposed VQA metric is lower than video signal-to-noise ratio, video quality model, motion-based video integrity evaluation, spatiotemporal most apparent distortion, V-BLINDS, and V-CORNIA metrics. Moreover, the proposed metric has a better generalization property than these metrics.

Toward Traffic Offload in Converged Satellite and Terrestrial Networks

Abstract: Whilst broadband Internet connectivity has become highly important, providing broadband connectivity nonetheless remains a considerable challenge, particularly in rural and remote regions where the deployment of optical fibers faces economical obstacles. A promising option to address this issue is that of the most recent satellite systems, capable of providing high capacities virtually everywhere. However, compared to most terrestrial systems, satellite networks have very different link and, more importantly, latency characteristics, which often render them only barely usable for delay intolerant traffic. Thus, convergence of terrestrial and satellite networks is required, so that only certain traffic flows can be offloaded onto a supplemental satellite connection. In this paper, we propose a network architecture relying on modern software defined network concepts, which enable dynamic traffic offloading in a converged satellite and terrestrial network, in order to relieve the load in a narrow-band terrestrial network. We show that with limited overhead, a traffic can be offloaded, leading to an increase in the user’s quality-of-experience.

Layered Division Multiplexing for ATSC 3.0: Implementation and Memory Use Aspects

Abstract: This paper presents implementation and memory use aspects for layered division multiplexing (LDM) technology defined in the next generation terrestrial broadcast standard, called advanced television systems committee (ATSC) 3.0. As LDM becomes a new method that combines multiple broadcast contents, its practical considerations on transmitter and receiver implementations as well as memory usages are described in this paper. When multiple physical layer pipes are used, the feasibility of the implementation and memory use aspects are discussed, and the performance analysis in comparison with other multiplexing techniques that ATSC 3.0 offers is shown.

Global Auto-Regressive Depth Recovery via Iterative Non-Local Filtering

Abstract: Existing depth sensing techniques have many shortcomings in terms of resolution, completeness, and accuracy. The performance of 3-D broadcasting systems is therefore limited by the challenges of capturing high-resolution depth data. In this paper, we present a novel framework for obtaining high-quality depth images and multi-view depth videos from simple acquisition systems. We first propose a single depth image recovery algorithm based on auto-regressive (AR) correlations. A fixed-point iteration algorithm under the global AR modeling is derived to efficiently solve the large-scale quadratic programming. Each iteration is equivalent to a nonlocal filtering process with a residue feedback. Then, we extend our framework to an AR-based multi-view depth video recovery framework, where each depth map is recovered from low-quality measurements with the help of the corresponding color image, depth maps from neighboring views, and depth maps of temporally adjacent frames. AR coefficients on nonlocal spatiotemporal neighborhoods in the algorithm are designed to improve the recovery performance. We further discuss the connections between our model and other methods like graph-based tools, and demonstrate that our algorithms enjoy the advantages of both global and local methods. Experimental results on both the Middleburry datasets and other captured datasets finally show that our method is able to improve the performances of depth images and multi-view depth videos recovery compared with state-of-the-art approaches.