Wi-Fi channel state information (CSI)-based sensing faces challenges in adapting to real-world scenarios due to technical constraints that hinder CSI acquisition. Compressed beamforming report (CBR), standard-compliant channel information in IEEE 802.11ac/ax, is being investigated as an alternative, device-agnostic information source for sensing. In this paper, we implement WiPiCap, a real-time CBR decoding system that is lightweight, easy to deploy, and supports arbitrary antenna, frequency, and bandwidth configurations. In the evaluation, we show that the motion of surrounding objects is reflected in a live plot of CBR data in real time.

Speculative execution in cloud gaming pre-renders predicted frames to mitigate network latency, but faces exponential computational costs and static prediction models that cannot adapt to player behavior. This paper proposes an adaptive retraining framework for LSTM-based pattern prediction that dynamically updates models using real-time user input. Experiments across three game genres demonstrate efficient pattern reduction, achieving performance comparable to that of pre-trained models with short training intervals.

Reducing response delay while maintaining high-quality visual and audio output is a critical challenge in cloud gaming services. Speculative transmission has been proposed to reduce the response delay for video frames, whereas the response delay reduction for spatial sound has remained underexplored. In this study, we propose a model-based speculative transmission scheme to deliver high-quality spatial sound with negligible response delay for cloud gaming services. By presharing compressed impulse responses (IRs) between the cloud server and the user’s low-end device using an implicit neural representation (INR) model, our scheme achieves significant traffic reduction while preserving nearly lossless spatial sound quality. In addition, the INR-based model uses a small neural network architecture to reduce the storage cost required for sharing compressed IRs on low-end user devices. Evaluation results using pyroomacoustics show that our scheme achieves a traffic reduction of more than 89.2% while maintaining nearly lossless spatial sound reconstruction.

This paper presents a novel knowledge-assisted joint source-channel coding (JSCC) scheme that integrates implicit neural compression (INC) with analog residual transmission for high-quality and efficient image delivery. To improve compression efficiency and decoding delay, we introduce a knowledge base architecture that separates learning and inference: the global knowledge, implemented on the cloud, compresses image-specific latent representations and lightweight network parameters; the local knowledge, deployed at the edge, reconstructs side information using lightweight inference from the received bitstream. Although the side information enables coarse reconstruction, it often lacks the fine-grained visual details, i.e., high-frequency components. To supplement the missing high-frequency details, the residuals between the original and side information are transmitted via pseudo-analog modulation and reconstructed at the receiver. The residual transmission not only enables high-quality reconstruction of high-frequency components but also provides graceful improvement under instantaneous channel conditions. Experimental results on the Kodak and CLIC2020 datasets demonstrate that the proposed scheme significantly outperforms conventional and learned image codecs in terms of reconstruction quality and robustness to unreliable channel conditions.

With the advancement of immersive services such as Extended Reality (XR), there is an increasing demand for high-quality and low-latency transmission methods for 3D point clouds. However, existing point cloud transmission methods face challenges such as long latency in the compression process, as well as degradation in visual quality. This study proposes a novel point cloud transmission method that simultaneously achieves high visual quality and low compression latency. Specifically, the proposed method determines the importance of each point in the point cloud based on the user’s viewpoint, and realizes point-wise adaptive compression by loading and discarding multiple 3D points in parallel using multiple processing cores. Experimental results on the 8iVFBv2 point cloud dataset demonstrate that the proposed method achieves a frame rate that is 17.3× higher than existing low-latency and user-viewpoint-based compression techniques, while maintaining comparable visual quality.

Efficient tactile communication will pave the way for future augmented reality services. Tactile coding solutions for vibrotactile signals have been proposed as one of the enabling technologies. However, due to the all-or-nothing behavior, such coding solutions cause destructive quality degradation, known as cliff and leveling-off effects, over unstable wireless channels. We propose a novel joint source-channel coding scheme for tactile communication. The proposed scheme transforms the tactile signals into frequency domain representations and transmits them to mitigate cliff and leveling-off effects. Moreover, the proposed scheme unequally scales each frequency domain representation based on human tactile perception to simultaneously realize error resilience against channel noise and maximize the quality of vibrotactile signals with respect to human tactile perception. Evaluations show that the proposed scheme mitigates the cliff and leveling-off effects even when the wireless channel quality fluctuates during vibrotactile communication.

Radar-based indoor 3D human pose estimation typically relied on fine-grained 3D keypoint labels, which are costly to obtain especially in complex indoor settings involving clutter, occlusions, or multiple people. In this paper, we propose \textbf{RAPTR} (RAdar Pose esTimation using tRansformer) under weak supervision, using only 3D BBox and 2D keypoint labels which are considerably easier and more scalable to collect. Our RAPTR is characterized by a two-stage pose decoder architecture with a pseudo-3D deformable attention to enhance (pose/joint) queries with multi-view radar features: a pose decoder estimates initial 3D poses with a 3D template loss designed to utilize the 3D BBox labels and mitigate depth ambiguities; and a joint decoder refines the initial poses with 2D keypoint labels and a 3D gravity loss. Evaluated on two indoor radar datasets, RAPTR outperforms existing methods, reducing joint position error by $34.3$\% on HIBER and $76.9$\% on MMVR. Our implementation is available at \url{https://github.com/merlresearch/radar-pose-transformer}.

This study aims to enhance the transmission efficiency of microwave power transfer, which has garnered significant attention as a method of wireless power supply for IoT devices and drones, by using Intelligent Reflecting Surface (IRS). IRS is a plate-shaped device that can control the reflection characteristics of radio waves, and it is expected to improve the transmission efficiency when placed between a wireless transmitter and a wireless receiver. However, existing methods of reflection phase optimization for IRS are predominantly based on sequential feedback or a small number of discrete reflection phases, both of which have problems, including feedback overhead and beam shape limitations. In this paper, we propose a novel reflection phase optimization method based on curve fitting. This approach is capable of continuous reflection phase control and optimization of the reflection phase of all reflective elements simultaneously. Through numerical simulation and actual experiments using an implemented IRS prototype, it was observed that the proposed method enhanced the received power by up to 4 dB compared to a conventional sequential optimization method. However, it was also observed that the range limitation of the reflection phase in the implemented IRS hindered the effectiveness of the proposed method.

Wi-Fi channel measurements across different bands, e.g., sub-7-GHz and 60-GHz bands, are asynchronous due to the uncoordinated nature of distinct standards protocols, e.g., 802.11ac/ax/be and 802.11ad/ay. Multi-band Wi-Fi fusion has been considered before on a frame-to-frame basis for simple classification tasks, which does not require fine-time-scale alignment. In contrast, this paper considers asynchronous sequence-to-sequence fusion between sub-7-GHz channel state information (CSI) and 60-GHz beam signal-to-noise-ratio (SNR)s for more challenging tasks, such as continuous coordinate estimation. To handle the timing disparity between asynchronous multi-band Wi-Fi channel measurements, this paper proposes a multi-band neural dynamic fusion (NDF) framework. This framework uses separate encoders to embed the multi-band Wi-Fi measurement sequences to separate initial latent conditions. Using a continuous-time ordinary differential equation (ODE) modeling, these initial latent conditions are propagated to the respective latent states of the multi-band channel measurements at the same time instances for a latent alignment and a post-ODE fusion, and at their original time instances for measurement reconstruction. We derive a customized loss function based on the variational evidence lower bound (ELBO) that balances between the multi-band measurement reconstruction and continuous coordinate estimation. We evaluate the NDF framework using an in-house multi-band Wi-Fi testbed and demonstrate substantial performance improvements over a comprehensive list of single-band and multi-band baseline methods.

Non-terrestrial networks (NTNs) contribute significantly to offering connectivity services for the upcoming sixth generation (6G). However, many services demand real-time uplink data transmission. Owing to the dynamic topology, instability of NTN channel status, resource constraints, and tremendous mobile users on ground networks; resilient and effective resource management and channel accessibility pose substantial challenges. This work investigates the uplink sum-rate maximization and latency minimization problems with a channel allocation scheme for user equipments (UEs) to high-altitude platforms (HAPs) of a particular NTN when UEs simultaneously transmit data to HAPs and HAPs forward it to low-earth orbit satellite (LEO-sat) via multiple subcarriers. We formulate a fairness resource allocation optimization problem reflecting the system utility and load of the HAP-LEO-sat link for aerial-to-space uplinks. To tackle the high-dimensional non-convex optimization problems and short visibility of LEO-sat in the learning, we advocate a novel deep reward-based multi-agent reinforcement learning (DeepRMARL) with prioritized experience replay (PER) and priority sampling approach (PSA). Furthermore, we present a long-short-term memory (LSTM) model for future environment channel prediction due to time-varying channels between UEs and HAPs. The results explicitly unveil the feasibility of our method in optimizing total uplink rate, achieving a higher fairness index, minimizing latency by approx. 12.2%, and predicting future environment states with approx. 92.8% accuracy.

Wireless sensing research is underway to generate 2D images and 2D videos corresponding to an object or space using the measured amplitude and phase changes during RF signal propagation. The obtained 2D images and 2D videos can be used for object recognition and distance measurement based on image processing techniques. However, 2D images only contain visual information about the sensing target from a specific viewpoint. This paper proposes Channel State Information to Point Cloud (CSI2PC) to enable the observation of a sensing target from multiple viewpoints. CSI2PC generates a 3D point cloud corresponding to the 3D structure of the sensing target from Channel State Information (CSI), which stores the variation of amplitude and phase. CSI2PC generates a 3D point cloud from the measured CSI using a neural network (NN) architecture based on Generative Adversarial Networks (GAN) and Graph Neural Networks (GNN). To ensure that the generated point clouds accurately represent the sensing target, the proposed scheme designs 1) a two-stage learning of the proposed NN architecture and 2) a loss function considering the 3D point cloud reconstruction. Experimental results using consumer Wi-Fi devices show that the proposed CSI2PC can reconstruct a clean point cloud from the measured CSI and accurately classify the object using the point cloud-based classification model.

Non-terrestrial networks (NTNs) are crucial for 6G communications but face challenges like classical communication bottlenecks. Semantic communication (SemCom) offers a solution, but efficient resource allocation remains unresolved. This paper proposes a joint approach for resource allocation and semantic information selection in low-earth orbit (LEO) satellite-based image transmission. A novel metric, Quality of Semantic (QoSem), is introduced to measure the SemCom performance. An optimization problem is formulated to maximize QoSem by allocating computing, power, and bandwidth resources and selecting optimal semantic information. Furthermore, we propose a novel deep reinforcement learning (DRL) algorithm, namely Mixed-Action Decision Intelligent Semantic-aware Soft-Actor Critic (MADIS-SAC), to handle the problem’s nonconvexity and NP-hard complexity. Results show 82.5% of QoSem improvement compared to baselines under resource constraints.

Point cloud delivery over wireless and mobile channels will be a key technology for untethered users to realize extended reality via wireless and mobile terminals. A key challenge of point cloud delivery is efficiently delivering the point cloud over unstable and band-limited channels. Graph Fourier Transform (GFT) is a potential solution to compress such non-uniformly and non-orderly distributed signals in a 3D space, whereas GFT-based solutions require large communication overhead to share the graph information with the receiver. This paper proposes a novel point cloud delivery scheme that introduces implicit neural representation (INR) to reduce the overhead. Specifically, the INR of the proposed scheme trains a mapping between the indices and the corresponding weight in the adjacency matrix and the proposed scheme sends the parameter set of the INR as the metadata. Evaluations demonstrate that the proposed scheme can improve the point cloud quality under the same amount of communication overhead because the proposed INR can predict most of the elements in the adjacency matrix using a small parameter set.

In contrast to existing multi-band Wi-Fi fusion in a frame-to-frame basis for simple classification, this paper considers asynchronous sequence-to-sequence fusion between sub-7GHz channel state information (CSI) and 60GHz beam SNR for more challenging downstream tasks such as continuous regression. To handle the timing disparity between the two channel measurements, we extend our recently proposed dual-decoder neural dynamic (DDND) framework with latent ordinary differential equations (ODEs), align the distinct latent dynamic states at the same time instances, and introduce a post-ODE fusion framework. The resulting neural dynamic fusion (NDF) framework is trained in an end-to-end fashion with a modified variational autoencoder loss function. Evaluation over a newly collected in-house multi-band Wi-Fi dataset shows the advantage of the proposed NDF method over frame-based and DDND methods.

Haptic communication will be a key technology for extended reality (XR), robotics, and remote manipulation. The deformation magnitude of 3D deformable objects is a key attribute for realizing fine-grained remote manipulation. The typical solutions for sending the deformation magnitudes over wireless channels are to perform digital compression and transmission considering the channel quality, i.e., digital source-channel coding. However, the key problems of the solutions are 1) still large traffic and 2) catastrophic quality degradation due to channel quality fluctuation. This paper proposes a graph-based analog joint source-channel coding for 3D haptic communication. Specifically, Graph Fourier Transform (GFT)-based energy compression efficiently removes the redundancy across deformation magnitudes. In addition, the integration of unequal error protection and analog modulation prevents catastrophic degradation and gradually improves the reconstruction quality according to the instantaneous channel quality. Evaluation results using the deformation magnitude of various 3D objects show that the proposed scheme prevents quality degradation due to channel quality fluctuations and provides accurate deformation magnitude for remote users.

This paper shares the challenges facing today's augmented reality (AR) smartphone applications, particularly in the realm of localization and tracking failure. Our research identifies limitations in current vision-based landmarks such as QR codes and AprilTags, commonly used to aid in localization, and the drawbacks of LiDAR integration in variable lighting conditions, compromising AR's accuracy and functionality. We also examine the constraints of Inertial Measurement Units (IMU) on movement speed, highlighting its impact on the dynamic performance of AR applications. Based on our extensive 316 experimental cases for 113 hours, including 34 case studies with 17 subjects in 2 sites, this paper presents the field with a nuanced analysis of the failure modes inherent in smartphone-based AR localization. We further explore a prototype solution which fuses ultra-wideband (UWB)-based sensing with the vision-based systems to alleviate these failure modes. Our approach addresses the immediate challenges of AR localization and opens avenues for future research and development in creating more spatially aware and interactive digital worlds. All of our demonstration videos, code, and datasets are available here1.

Intelligent reflecting surface (IRS) has emerged as a promising communication technology to increase throughput and facilitate communication in non-line-of-sight scenarios. While existing studies primarily focus on the application of IRS in communication tasks, its unique capability to precisely control radio frequency ($\mathbf{R F}$) signal reflections offers untapped potential for non-communication purposes. This paper ventures into an innovative domain and explores the application of IRS properties for image processing tasks conducted over the air. We introduce a novel scheme called “single antenna imaging”, a proof-of-concept scheme for over-the-air image processing. This approach leverages analog modulation and compressed sensing techniques to reconstruct high-quality images from a limited number of transmissions between multiple transmit antennas and a single receive antenna. Evaluations using the Kodak image dataset show that the proposed scheme can retrieve the same quality images with over 40% reduction in traffic compared to the simple minimum norm solution (MNS) and linear minimum mean square error (LMMSE) schemes.

Conventional point cloud delivery schemes employ tree-based or graph-based digital compression techniques to stream three-dimensional (3D) points and their associated attributes over wireless multiple-input multiple-output (MIMO) channels for 3D scene reconstruction. However, these digital-based delivery methods suffer from the cliff and leveling effects. Moreover, in MIMO communication environments, poor quality in some spatial layers, i.e., subchannels, can lead to point cloud quality degradation. We propose a novel point cloud delivery scheme that addresses both effects simultaneously. Our approach leverages a k-dimensional tree (k-d tree)-based graph Fourier transform (GFT) for energy compaction, a subchannel assignment mechanism to fully utilize subchannel diversity in MIMO links, and analog modulation with non-uniform power allocation for error-resilient and fluctuation-resilient transmission. Evaluation results demonstrate that the proposed scheme delivers superior 3D reconstruction quality compared to conventional digital-based point cloud delivery schemes, such as geometry-based point cloud compression (G-PCC), in noisy MIMO channels.

A long network delay leads to decreased player performance in online games and an unfair game experience between the players. In this study, we propose a novel delay compensation method to reduce the impact of network delay on player performance in online 3D shooting games. The key contributions of the proposed scheme are two-fold. The first contribution utilizes two deep reinforcement learning (DRL) networks, timing and direction DRL, to predict each player's sudden movement under the network delay. Specifically, the timing DRL outputs when the player will change the movement direction while the direction DRL outputs the indenting direction of the player. The second contribution of this study is to send each player's trained model to other players at the beginning of the game match. This research is the first to discuss how to transfer the trained model, as well as the effect of the model transmissions on the delay compensation performance in the band-limited paths. We evaluated the effectiveness of the proposed delay compensation using an online 3D shooting game implemented by Unity 3D. The obtained evaluation results indicate that the proposed delay compensation accurately reproduces the player's position, even under long network delays. In addition, the transmitted model can predict the player's position when the available rate for the model transmission is approximately 60 KB.

Viewport-based 360-degree video delivery is a typical method to reduce video traffic for virtual reality (VR) applications. However, viewport-based solutions cause some key issues in multi-user VR applications, including large video traffic volumes owing to redundant video transmission across multiple headset users and quality degradation owing to view-port transition. In this study, we propose a 360-degree video delivery scheme for multi-user VR applications. To overcome the abovementioned issues, the proposed approach includes JPEG-XS-based video recompression at the edge server to follow the viewport transition and hybrid unicast and multicast tile delivery to prevent redundant transmissions. Moreover, we present the results of evaluations using 360-degree video and corresponding fixation points of multiple users to show that the proposed scheme prevents redundant transmissions across multiple headset users and provides better video quality for each user than existing solutions for the same video traffic.

The evolution of a cell phone network and a wireless local area network (LAN) has enabled various devices to be connected to wireless networks anytime, anywhere. This has led to the emergence of various applications, such as automatic transportation of parts and cargo by automated guided vehicles, remote control of unmanned vehicles, and drones. It will be a challenge to maintain high-quality connections, which satisfy the requirements of such emerging applications, for a large number of intelligent connected devices. To improve the network performance, we must control the wireless network setting and the behavior of the connected vehicles, i.e., robots. From this perspective, this proposes a novel framework, CoRein, that achieves simultaneous optimization of robot behavior and wireless network setting. In contrast to the existing studies, CoRein provides optimal actions for robots and wireless networks using deep reinforcement learning (DRL) architecture. To the best of our knowledge, our study firstly attempts to solve different tasks corresponding to robots and networking with a unique DRL architecture. To evaluate the effectiveness of CoRein, we considered a scenario where two robots were moving back and forth while communicating with one of two access points. CoRein calculates the robot velocities and AP selection. We carried out simulations and indoor experiments with our robot testbeds to evaluate the network performances. Evaluation results confirmed that the two robots adjusted their positions and the access points to increase the wireless network's performance, i.e., throughput and round trip time while changing the speed of their movements.

A key challenge in holographic-type communication is transmitting point cloud signals to users across diverse channel quality and bandwidths. Digital based point cloud coding efficiently reduces point cloud traffic. In contrast, the quantization and entropy coding in digital-based schemes causes quality degradation owing to channel quality fluctuations and diversity. This study proposes a novel scheme for point cloud delivery over wireless channels. The proposed scheme consists of a graph auto-encoder (GAE) architecture to compress the point cloud into coded symbols and restore the point cloud from the received symbols. The proposed scheme addresses the quality degradation due to channel quality fluctuations and bandwidth diversity via, the following two steps. First, the coded symbols are directly mapped onto transmission symbols, analog modulation, to ensure that the point cloud quality follows the instantaneous channel quality of each user. Second, a non-uniform dropout is introduced to realize a rateless property in the GAE architecture for gradually improving the point cloud quality according to the available bandwidth. Evaluation results demonstrate that the proposed GAE architecture can yield better point cloud quality than digital-based and analog-based schemes, even when users have varying available bandwidths.

Sending tactile information over wireless channels will pave the way for the realization of immersive Extended Reality (XR) applications. Existing vibrotactile communication solutions have integrated traditional source and channel coding to provide high-quality vibrotactile information over band-limited and error-prone wireless channels. However, due to the bit-error vulnerability of source and channel coding and the unrecoverable distortion of source coding, the existing solutions suffer from catastrophic quality degradation. This paper proposes a novel tactile communication scheme inspired by semantic communication. The main contribution to the existing solutions is to replace the source and channel coding with the semantic vibrotactile coder and transceiver. Specifically, the semantic encoder compresses the vibrotactile information using a two-dimensional discrete cosine transform (2D-DCT) without binarization, and the transmitter directly maps the DCT coefficients to the transmission symbols with the optimized power allocation to avoid the catastrophic quality degradation due to the traditional source and channel coding and to maximize the vibrotactile quality under the wireless channel condition. In addition, we design an overhead-less semantic receiver to reduce the communication overhead required for the optimized power allocation. We implement a vibrotactile acquisition system for empirical evaluations, and the results showed that the proposed scheme provides better vibrotactile quality even in low-quality and high-quality wireless channels compared to the tactile communication schemes with the traditional source and channel coding.

Collaborative learning has been qualitatively analyzed by learning science researchers to enhance learning performance. A quantitative analysis system supports the existing qualitative analysis of collaborative learning. We propose an Internet of Things (IoT) system comprising business-card-type badges, radio-over-fiber (RoF)-based synchronization, and a collaboration analysis algorithm to support collaborative learning analytics in different venues (e.g., offline, online, and hybrid). We showed that our proposed system quantitatively supports qualitative analysis of collaborative learning in different environments.

Holographic-type communication, i.e., three-dimensional (3D) content delivery, will be a crucial application for modern wireless and mobile networks. In this paper, we propose a novel soft delivery scheme to realize efficient 3D content delivery. Specifically, the proposed scheme sends a single 2D image over error-prone wireless channels using discrete cosine transform followed by near-analog modulation. At the receiver, a 2D-to-3D decoder based on graph neural networks (GNN) reconstructs the corresponding 3D point cloud and mesh from the received 2D image. We verify that the proposed soft 2D-to-3D delivery scheme can reconstruct clean 3D data gracefully from the soft-delivered 2D image even in the presence of fading and noise distortion. In addition, the proposed scheme can generate higher-quality 3D data compared with direct 3D content delivery schemes.

Backscatter technology reduces power consumption during data transmission to 1/1000 that of existing wireless transmitters. However, backscatter communication faces two challenges related to medium access control (MAC). First, the carrier wave is not always supplied by an external device when the backscatter tag attempts to transmit data. In addition, if multiple backscatter tags communicate simultaneously, their outgoing frames collide with each other. This study proposes an asynchronous MAC protocol for backscatter tags without a receiving function. The proposed MAC protocol consists of a pseudo-synchronization system and a devised transmission interval between the carrier wave and each backscatter tag. The success rate of data transmission is improved by devising a carrier supply interval and backscatter tag operation. In addition, frame collisions decrease when using a pseudo-random number series to set the transmission interval for each backscatter tag. In a pseudo-synchronization system, the receiver feedbacks time information to the carrier transmitter when the receivers receive or detect a frame. The carrier transmitter adjusts the timing of the carrier supply to match the frame transmission from the backscatter tag, based on the information fed back. This improves the success rate of the communication without time synchronization. The simulation evaluation reveals that the proposed method improves the communication success rate by approximately 1.43 times in the assumed environment.

3D point cloud data formats are used to express three-dimensional (3D) information using numerous points in a 3D space. A key challenge is the delivery of high-quality 3D point cloud for the users under a diverse channel quality and available bandwidth to share the same 3D space across multiple untethered extended reality (XR) users. The existing digital-based schemes suffer from two issues owing to the diversity: cliff and leveling-off effects. This paper proposes a novel soft multicasting scheme of point cloud data for untethered XR users. The key ideas of the proposed scheme are three-fold: 1) integration of graph signal processing and analog modulation to adaptively improve the 3D reconstruction quality according to the channel quality for all individual XR users, 2) integration of Givens rotation and non-uniform adaptive quantization to reduce metadata overhead for the graph Fourier transform, and 3) prioritized transmission of the metadata to realize adaptive quality improvement based on the bandwidth available for each XR user. This paper reveals that the proposed scheme prevents cliff and leveling-off effects even when the XR users experience different channel qualities. Furthermore, the proposed transmission exhibits better 3D reconstruction quality compared with the state-of-the-art graph-based delivery scheme in band-limited environments.

In backscatter communications, data is transmitted with up to 1/1000 of the power consumption, instead of the typical several tens of milliwatts. However, if there are multiple nodes for backscatter communication in the same space, harmonics and sidebands limit the number of multiplexing nodes. Both are suppressed by switching the impedance in multiple steps or generating the signal with opposite phases; however, these methods increase the hardware cost of the backscatter tag. This paper proposes harmonics-controlled frequency division multiple access (HC-FDMA), which allows multiple access interference in backscatter communications without hardware modification. HC-FDMA consists of two modules: a modulation scheme and a channel selection algorithm. The modulation scheme controls the harmonics to an arbitrary channel using ∆Σ modulation. The channel selection algorithm optimizes the frequencies of the signal component, harmonics, and sidebands. Targeting IEEE 802.15.4-compatible backscatter, backscatter communication using HC-FDMA achieves 1.45 times multiplexing compared to the case without HC-FDMA. In addition, the analysis results characterize the capability of HC-FDMA.

Multicast-based multimedia streaming is expected to be a key application in the next generation of wireless and mobile networks. For such applications, we utilize hierarchical non-terrestrial networks (NTNs) consisting of low earth orbit (LEO) satellites, high-altitude platforms (HAPs), and user equipment (UE). However, existing multicast protocols for NTN cause a long delay and jitter owing to packet loss, thus being the cause of low-quality streaming services. We propose a reliable multi-cast protocol, namely, overhearing and non-orthogonal multiple access (NOMA) repairing reliable multicast (ONRM), for delay reduction. In ONRM, the HAP overhears packets from the LEO to UE and retransmits the lost packets using NOMA on behalf of the LEO to prevent delay increments. We carried out evaluations based on the release of Third Generation Partnership Project (3GPP) in terms of delay, throughput, and jitter. We prepared existing multicast protocols, pragmatic general multi-cast (PGM), reliable and efficient multicast protocol (REMP), and extended bit-indexed explicit replication (extended BIER) for comparison. Results show that ONRM outperforms other multicast protocols. Especially, the proposed ONRM can realize a low jitter and enables high-quality multimedia streaming over NTN.

Point cloud is an emerging data format useful for various applications such has holographic display, autonomous vehicle, and augmented reality. Conventionally, communications of point cloud data have relied on digital compression and digital modulation for three-dimensional (3D) data streaming. However, such digital-based delivery schemes have fundamental issues called cliff and leveling effects, where the 3D reconstruction quality is a step function in terms of wireless channel quality. We propose a novel scheme of point cloud delivery, called HoloCast+, to overcome cliff and leveling effects. Specifically, our method utilizes hybrid digital-analog coding, integrating digital compression and analog coding based on graph Fourier transform (GFT), to gracefully improve 3D reconstruction quality with the improvement of channel quality. We demonstrate that HoloCast+ offers better 3D reconstruction quality in terms of the symmetric mean square error (sMSE) by up to 18.3 dB and 10.5 dB, respectively, compared to conventional digital-based and analog-based delivery methods in wireless fading environments.

In typical point cloud delivery, a sender uses octree-based and graph-based digital video compression to send three-dimensional (3D) points and color attributes. However, the digital-based schemes have an issue called the cliff effect, where the 3D reconstruction quality will be a step function in terms of wireless channel quality. To prevent the cliff effect subject to channel quality fluctuation, we have proposed a wireless point cloud delivery called HoloCast inspired by soft delivery. Although the HoloCast realizes graceful quality improvement according to instantaneous wireless channel quality, it requires large communication overheads. In this paper, we propose a novel scheme for soft point cloud delivery to simultaneously realize better 3D reconstruction quality and lower communication overheads. The proposed scheme introduces an end-to-end deep learning framework based on graph neural network (GNN) to reconstruct high-quality point clouds from its distorted observation under wireless fading channels. We demonstrate that the proposed GNN-based scheme can reconstruct a clean 3D point cloud with low overheads by removing fading and noise effects.

In this paper, we present an experimental evaluation of the several phase optimization algorithms for distributed cooperative microwave power transfer. The proposed evaluation uses multiple Tx antennas distributed in a space. When the Tx antennas are in the far field from each other, appropriate phase control can generate constructive interference at the target location. The phase optimization algorithm optimizes the phase of each Tx antenna based on the received-signal-strength indication (RSSI) feedback from the target. The experimental results indicate that the RSSI measurement resolution and the controllable phase resolution at each Tx antenna affect the performance of the phase optimization.

Screen-camera communications, using a liquid crystal display (LCD) screen and camera image sensors, have been attractive variants of visible light communications (VLC) since any external light-emitting modules and photo detectors are required for recent mobile devices, which are usually equipped with display and camera. A major issue in screen-camera communications is a performance loss in transmission rate due to nonlinear channel impairments with ambient noise. To improve transmission rates, we investigate the impact of nonlinear channel equalization, nonbinary channel coding, probabilistic shaping, and nonlinear precoding for high-order modulation schemes. Experimental evaluations using an LCD screen and camera demonstrate that our proposed scheme achieves 3.8-3.3 times higher transmission rates compared to existing schemes for a communication distance of 60-160 cm.

Wireless communications for wireless devices require a significant power consumption for signal amplification. Such a significant power consumption may induce short lifetime of battery-operated wireless devices, especially, sensor devices and Internet of Things (IoT) devices. Backscatter communications have been proposed in recent years for ultra-low-power wireless communications, e.g., Wi-Fi. In this study, we propose and implement a novel backscatter communication scheme for sensor devices, i.e., IEEE 802.15.4-compatible backscatter. The proposed backscatter scheme consists of three devices: RF signal generator, backscatter transmitter, and standard IEEE 802.15.4 receiver. Specifically, the backscatter transmitter receives the RF signals with/without frequency hopping emitted from the RF signal generator and modulates the received signals into IEEE 802.15.4 packets by switching the impedance of the transmission antenna. Since the backscatter transmitter does not need signal amplification, instead, only needs to switch on/off state of the transmission antenna for packet transmissions, the power consumption of the proposed scheme is significantly lower than the conventional IEEE 802.15.4-based schemes. From the experimental evaluations, we demonstrated that the proposed backscatter scheme can realize IEEE 802.15.4 packet transmissions with low power at a certain angle and communication distance. In addition, we also clarified the frequency hopping at the RF signal generator can increase the number of the received packets since it may reduce the dead spots due to multi-path fading.

Soft image delivery uses pseudo-analog modulation for wireless image transmissions to prevent cliff and leveling effects subject to channel quality fluctuation and to realize graceful quality improvement according to wireless channel quality. Despite its attractive feature of graceful performance, the conventional soft image delivery suffers from low image quality when the analog-modulated symbols are severely impaired by fading and strong channel noise. In this paper, we propose a novel scheme of soft image delivery to reconstruct high-quality images from its low-quality observations. Specifically, the proposed scheme integrates deep convolutional neural network (DCNN)-based image restoration, i.e., deep image prior, into soft image delivery. The deep image prior learns a mapping function from the noisy image to the clean image based on user's perception-aware loss function using multiple training images in prior to soft delivery. The mapping function can restore a clean image even when the received image is distorted by strong fading and additive noise. From the evaluation results, the proposed scheme can remove fading and noise effects from the received images by using DCNN-based image restoration. For example, the proposed scheme achieves up to 0.44 improvement compared with the conventional soft image delivery in terms of structural similarity (SSIM) index at a deep fading channel.

Conventional point cloud delivery schemes use graph-based compression to stream three-dimensional (3D) points and the corresponding color attributes over wireless channels for 3D scene reconstructions. However, the graph-based compression requires a significant communication overhead for graph signal decoding, i.e., inverse graph Fourier transform (IGFT), and such large overhead causes a low 3D reconstruction quality due to power and rate losses. We propose a novel scheme of point cloud delivery to significantly reduce the amount of overhead while allowing a small degradation in the 3D reconstruction quality. Specifically, the proposed scheme exploits Givens rotation for the graph-based transform basis matrix to compress the basis matrix into quantized angle parameters. Even when the angle parameters are strongly quantized for compression, the receiver can reconstruct a clean point cloud by using the basis matrix obtained from the quantized angle parameters. Evaluation results show the Givens rotation in the proposed scheme achieves overhead reduction with a slight quality degradation. For example, the proposed scheme achieves 89.8% overhead reduction with 1.3 dB quality degradation compared with the conventional point cloud delivery scheme.

Wireless 360-degree video delivery provides virtual reality (VR) immersive experience where each user can freely switch his/her viewing orientation. The existing schemes of the wireless 360-degree video streaming use digital-based compression and transmission. However, they have many disadvantages in terms of video traffic and quality: cliff effect due to unstable wireless channels, large video traffic due to the extremely high resolution of the 360-degree video, and the perceptual redundancy within the transmitted video. To solve the above problems, this paper proposes a novel wireless 360-degree video transmission scheme called 360Cast. 360Cast adopts the analog-based transmission, including power allocation and analog modulation, to achieve graceful video quality improvement even in time-varying wireless channels. Here, the power allocation considers the distortion of human perception and sphere-to-plane projection to maximize the human perceptual quality at the 360-degree video playback. In addition, 360Cast predicts and transmits the user's viewport based on the recent HMD user's orientation by using dynamic linear regression (DLR) and only transmits the viewport for traffic reduction. Evaluation results show that the proposed 360Cast provides better human perceptual quality via HMD compared with the existing analog transmission schemes, i.e., SoftCast and FoveaCast, by using the integration of viewport prediction, power allocation, and analog modulation.

Simultaneous screen-to-camera and screen-to-eye communications, i.e., watermarking, have been proposed in visible light communications. The main purpose of such communications is to provide many data bits for camera devices and visual information for human eyes by using a common displayed image. To this end, the existing studies leverage the capability discrepancy and distinctive features between the human vision system and camera devices. However, the existing techniques mainly require high refresh rates in both screen and camera devices to achieve better throughput while keeping high visual quality. In this paper, we propose a novel transmission scheme for efficient simultaneous screen-to- camera and screen-to-eye communications without a need of high refresh rates. Specifically, we use deep convolutional neural networks (DCNN)-based watermark encoder and decoder to embed many bits into high-quality images, and then to maximize throughput from the bit-embedded image. With end-to-end adversarial learning, the encoder networks learn a mapping function to embed digital data into an original image based on a perceptual loss function while the decoder networks also learn a mapping function from the bit-embedded image to the data bits based on a cross-entropy loss function. From the evaluations, we show that the proposed watermark encoding and decoding networks yield high throughput from the bit-embedded images compared with a simple DCNN-based watermarking. In addition, the bit-embedded images on the screen achieve high quality for human perception.

Soft delivery, i.e., analog transmission, has been proposed to provide graceful video/image quality even in unstable wireless channels. However, existing analog schemes require a significant amount of metadata for power allocation and decoding operations. It causes large overheads and quality degradation due to rate and power losses. Although the amount of overheads can be reduced by introducing Gaussian Markov random field (GMRF) model, the model mismatch can degrade reconstruction quality. In this paper, we propose a novel analog transmission scheme to simultaneously reduce the overheads and yield better reconstruction quality. The proposed scheme uses a deep neural network (DNN) for metadata compression and decompression. Specifically, the metadata is compressed into few variables using the proposed DNN-based metadata encoder before transmission. The variables are then transmitted and decompressed at the receiver for high-quality video/image reconstruction. Evaluations using test images demonstrate that our proposed scheme reduces overheads by 80.0% with 11.2 dB improvement of reconstruction quality compared to the existing analog transmission schemes.

In conventional point cloud delivery, a sender uses octree-based digital video compression to stream three-dimensional (3D) points and the corresponding color attributes over band-limited links, e.g., wireless channels, for 3D scene reconstructions. However, the digital-based delivery schemes have an issue called cliff effect, where the 3D reconstruction quality is a step function in terms of wireless channel quality. We propose a novel scheme of point cloud delivery, called HoloCast, to gracefully improve the reconstruction quality with the improvement of wireless channel quality. HoloCast regards the 3D points and color components as graph signals and directly transmits linear-transformed signals based on graph Fourier transform (GFT), without digital quantization and entropy coding operations. One of main contributions in HoloCast is that the use of GFT can deal with non-ordered and non-uniformly distributed multidimensional signals such as holographic data unlike conventional delivery schemes. Performance results with point cloud data show that HoloCast yields better 3D reconstruction quality compared to digital-based methods in noisy wireless environment.

Multi-homed wireless video streaming systems concurrently send video frames of one video content from multiple wireless access networks. The coexistence of Long Term Evolution (LTE) and Wi-Fi systems is one of the typical multi-homed wireless environments. To utilize such multiple wireless access links for high-quality streaming, we need to overcome two issues: rate decrement in the LTE channel due to cellular congestion and channel quality fluctuation in Wi-Fi channels due to noise, interference, and channel fading. To this end, we propose a hybrid digital-analog video transmission scheme, which integrates digital video coding, near-analog modulation, and compressive sensing, considering Wi-Fi offloading. To avoid an effect of the cellular congestion, the proposed scheme offloads a part of digital-encoded video data, which are originally transmitted in the LTE channel, to available Wi-Fi channels if LTE's data rate becomes insufficient during the streaming session. In addition, the proposed scheme uses a combination of compressive sensing and near-analog modulation for video delivery in Wi-Fi channels to ensure the received video quality can be proportionally improved according to the instantaneous quality of the Wi-Fi channels. From evaluations, the proposed Wi-Fi offloading technique keeps better video quality even when the capacity of LTE channel becomes limited within a streaming session. In addition, it was found that the received video quality can be gracefully improved according to the number of receivable Wi-Fi access points and each Wi-Fi channel quality.

In this paper, a traffic reduction scheme for videobased call and chat applications that uses deep neural network (DNN) based super resolution is proposed. Specifically, a sender transmits low-quality, low-resolution video frames containing face information in order to reduce the amount of video traffic. The receiver uses DNN-based super resolution to reconstruct highquality, high-resolution video frames from the low-quality video frames. The proposed scheme makes two contributions. First, face features are adopted for parameter optimization of DNNbased super resolution for high-quality image reconstruction, and second, the scheme includes a newly designed loss function that considers face features that allow high-quality face-containing video frames to be reconstructed at the receiver. According to our evaluation results using real video frames of video calls, the proposed scheme reduces the amount of video traffic by more than 90% as compared with conventional schemes that implement the standard video encoder. In this case, the proposed scheme achieves a reconstructed image quality up to 0.85 in terms of structural similarity (SSIM).

Wireless multi-view plus depth (MVD) video streaming enables free viewpoint video playback on wireless devices, where a viewer can freely synthesize any preferred virtual viewpoint from the received MVD frames. Existing schemes of wireless MVD streaming use digital-based compression to achieve better coding efficiency. However, the digital-based schemes have an issue called the cliff effect, where the video quality is a step function in terms of wireless channel quality. In addition, parameter optimization to assign quantization levels and transmission power across MVD frames are cumbersome. To realize high-quality wireless MVD video streaming, we propose a novel graceful video delivery scheme, called FreeCast. FreeCast directly transmits linear-transformed signals based on 5-D discrete cosine transform, without digital quantization and entropy coding operations. In addition, we exploit a fitting function based on a multidimensional Gaussian Markov random field model for overhead reduction to mitigate rate and power loss due to large overhead. The proposed FreeCast achieves graceful video quality with the improvement of wireless channel quality under a low overhead requirement. In addition, the parameter optimization to achieve highest video quality can be simplified by only controlling a transmission power assignment. Performance results with several test MVD video sequences show that FreeCast yields better video quality in band-limited environments by significantly decreasing the amount of overhead. For instance, structural similarity (SSIM) performance of FreeCast is approximately 0.127 higher than the existing graceful video delivery schemes across wireless channel quality, i.e., signal-to-noise ratio, of 0-25 dB at a transmission symbol rate of 37.5 Msymbols/s.

In 360-degree video streaming, a user may watch a part of video frames, namely, viewport, through an interactive display, e.g., head-mounted display, for the immersive experience. One of major issues for high-quality 360-degree video delivery is how to reduce perceptual redundancy in video frames according to user's viewing viewport. To this end, an existing scheme divides video frames into multiple tiles and adaptively encodes each tile based on user's viewing viewport using digital-based video compression. However, the video compression causes an issue in wireless channel called cliff effect, which causes sudden quality degradation in user's viewport and poor immersion in immersive applications. To reduce perceptual redundancy without cliff effect in wireless 360-degree video delivery, we propose a novel transmission scheme. Our scheme skips quantization and entropy coding, instead, directly transmits linear-transformed signals based on three-dimensional discrete cosine transform (3D-DeT) or the combination of one-dimensional DeT (1D-DeT) and spherical wavelet transform (SWT). By skipping both operations, the quality of viewport can be simply enhanced by its power allocation since it is directly applied at the pixel level instead of the bit level. It is demonstrated with 360-degree video sequences that the proposed scheme offers a great advantage over the conventional digital-based schemes.

In conventional multi-view video systems using progressive download, each user downloads only a desired viewpoint for traffic reduction; however, playback stalls occur after every view switching. The playback stalls induce low user satisfaction for such multi-view applications. A progressive download-based multi-view video scheme assuming successive view switching has been proposed to reduce video traffic and prevent playback stalls. However, when the user switches to a viewpoint other than adjacent viewpoints, this still causes playback stalls when watching the desired viewpoint. In particular, when a user randomly switches an observed viewpoint to find the preferred viewpoint, i.e., zapping, it needs to send videos of all viewpoints to accept zapping. This also causes playback stalls owing to sharp traffic growth. In this paper, we propose a novel progressive download-based multi-view video delivery scheme to decrease the number of playback stalls even when the user switches to an observed viewpoint other than adjacent viewpoints, including zapping. The main idea of the proposed scheme is to execute segment download scheduling by taking into account the view switching probability and viewpoint popularity of past users. Specifically, a user node foresees a user's view switching behavior in future video frames based on those information and schedules a download order of priority segments, which will be highly likely to be switched by the user, for smooth view switching. Evaluations using Joint Multi-view Video Coding (JMVC) encoder and multiview video sequences show that our scheme decreases the number of playback stalls.

Exploiting the inter-camera correlation among the crowdsourced mobile video contributors is desirable as it can improve the coding efficiency and/or video quality. This can be achieved by using a digital video encoder such as H.264/Multi-view Video Coding (MVC). However, highly-complex nature of digitalbased video encoding requires a large amount of power, which is a limited resource of the consumer-grade mobile devices. In addition to video encoding, transmission power is also consumed for video uploading based on the amount of uploading video traffic to satisfy video quality requirement. In this paper, we propose a powerefficient video uploading scheme for crowdsourced multi- view video streaming considering power-saving in both video encoding and video transmission. To this end, we design a cluster-based redirect transmission scheme. In this scheme, video frames of multiple contributors are only encoded by four-dimensional discrete cosine transform (4D-DCT) and modulated by near-analog modulation at a cluster head. By removing digital-based coding and modulation, our scheme achieves the same video quality with the existing scheme under low power requirements. Evaluation results show that the proposed scheme outperforms the digital-based video uploading schemes in terms of video quality and the amount of video traffic to achieve a targeted video quality.

A major challenge of screen-camera visual multiinput multi-output (MIMO) communications is to increase the achievable throughput by reducing nonlinear channel effects including perspective distortion, ambient lights, and color mixing. To mitigate such nonlinear effects, an existing transmission method uses linear or simple nonlinear equalizations in decoding operations. However, the throughput improvement from the equalization techniques is often limited because the effects are composed of a combination of various nonlinear distortions. In addition to the above issue, the existing studies consider specific environments, such as indoor and static communications, although screen-camera communications can be used for a variety of applications including outdoor and mobile scenarios. In this study, we propose 1) deep neural network (DNN)- based decoding for screen-camera communications to increase the achievable throughput and 2) Unity 3D-based evaluation methodology to synthetically learn the DNN for being robust against many different screen-camera environments. The DNN finds the best nonlinear kernels for equalization from numerously captured images, and then decodes original bits from newly captured images based on the trained nonlinear kernels. In the Unity-based evaluation tool, we can easily capture numerous photo-realistic images in different screen-camera scenarios to learn the impact of perspective distortion, screen- to-camera distance, motion blur, and ambient lights on the throughput since Unity-based environment can freely set programmable screens, cameras, and ambient lights on a 3D space. As an initial proof of concept, we demonstrate that the proposed DNN-based decoder scheme improves the achievable throughput by up to 148% compared to existing methods by equalizing nonlinear effects.

Wireless video delivery using multiple transmission paths has been studied in recent years to improve the received video quality. Hybrid WiFi-VLC is one of the promising multi- path techniques for indoor communications. Hybrid WiFi- VLC uses radio and visible light bands for transmissions. When a sender simply transmits videos over the hybrid path, the received video quality will be low due to two features of the visible light band. First one is the quality of visible light path will be significantly changed depending on the position and angle of devices, unlike the conventional radio communication. The second one is visible light path is typically one-way link. It means the conventional schemes have not been possible to provide video contents of adequate quality for each user. In order to address the above-mentioned issues, this paper proposes a video transmission scheme for hybrid WiFi-VLC environments. The proposed scheme integrates two-dimensional discrete wavelet transform (2D-DWT), digital video coding, and pseudo-analog modulation to provide baseline quality over radio paths and realize graceful quality enhancement according to the quality of visible light paths without the channel state information. Evaluations show that the proposed method improves PSNR performance by 11 dB compared to a simple method of dividing and transmitting video data over the radio and visible light paths across signal-to-noise ratios of 0 to 25 dB.

Soft video delivery, i.e., analog video transmission, has been proposed to provide high video quality in unstable wireless channels. However, existing analog schemes need to transmit a significant amount of metadata to a receiver for power allocation and decoding operations causing large overhead and quality degradation due to rate and power losses. To reduce the overhead while keeping the video quality high, we propose a new analog transmission scheme. Our scheme exploits a Gaussian Markov random field for modeling video sequences to significantly reduce the required amount of metadata, which are obtained by fitting into the Lorentzian function. Our scheme achieves not only reduced overhead but also improved video quality, by using the fitting function and parameters for metadata. Evaluations using several test video sequences demonstrate that the proposed scheme reduces overhead by 99.7% with 1.2-dB improvement of video quality (in terms of peak signal-to-noise ratio) compared to the existing analog video transmission scheme. We also investigate the impact of bandwidth limitation, showing a significant gain up to 2.7 dB for narrow-band systems.

Existing studies on multiview video streaming are proposed for terrestrial environments. They can be classified into request-reply and all camera transmission models, while both models suffer from a long switching delay and low video quality in an underwater acoustic path. The implementation of multiview video transmission in an underwater acoustic path requires reduction of the switching delay and the maintenance of high video quality. To this end, we propose a video transmission scheme, namely, Dolphin. Dolphin comprises time-shifted slot assignment, stochastic transmission, correlation-based compensation, and greedy transmission. Dolphin assigns asymmetric and time-shifted transmission slots to an encoder node and a user node according to a propagation delay. Stochastic transmission sends a potential camera's video that will be the most likely to be played back by the user before playback. If the stochastic transmission fails, the video frames of the user's desired camera are encoded with the mispredicted video frames to prevent switching delay from increasing. Moreover, greedy transmission sends all potential video frames, which are potentially displayed by a user, and determines rate allocation of the video frames based on the display probability. Evaluations using Mitsubishi Electric Research Laboratories' benchmark test sequences demonstrated that Dolphin achieved a short switching delay with a slight degradation in video quality irrespective of communication distance and acoustic path quality. For example, Dolphin decreases the switching delay by 92.7% compared with an existing request-reply model with 0.4-dB quality degradation at a communication distance of 300 m when a user tends to gaze at one camera.

Real-time multi-view video streaming plays an important role in new interactive and augmented video applications such as telepresence, remote surgery, and entertainment. Multiview video streaming approaches exploit time and inter-camera domain correlations in video frames for compression, thereby obtaining two benefits: video traffic reduction and video quality maintenance. However, excessive video encoding requires long periods of time for encoding/decoding computation in real-time multi-view video streaming and this process is vulnerable to frame loss, both of which may induce stalling and skipping during video playback at the user node. To address these problems, we propose Adaptive Multi-view Video Streaming (AMVS) system for real-time streaming. AMVS employs a greedy algorithm to adaptively select a prediction structure with low encoding/decoding computation based on the available network bandwidth. AMVS encodes and transmits multi-view video by considering the similarity of the user's request and the efficiency of transmission. Evaluations using Joint Multiview Video Coding (JMVC) demonstrated that AMVS achieves low encoding/decoding computation and high video quality while satisfying bandwidth limitations.

Wireless video streaming for multi-view plus depth (MVD) frames enables free viewpoint video on wireless devices, where a viewer can freely synthesize any preferred virtual viewpoint from the received MVD frames. To transmit MVD frames over wireless links, existing schemes use digital-based compression to achieve better compression efficiency. However, the digital-based schemes have an issue called cliff effect, where the video quality is a step function in terms of wireless channel quality. In addition, parameter optimization to allocate quantization levels and transmission power across MVD frames is cumbersome. To facilitate the MVD video streaming, we propose an analog-based transmission scheme. Our scheme directly transmits linear-transformed signals based on five-dimensional discrete cosine transform (5D-DCT), without relying on quantization and entropy coding. The proposed scheme achieves graceful video quality with the improvement of wireless channel quality. In addition, the parameter optimization to achieve highest video quality can be simplified by only controlling transmission power assignment. It is demonstrated with test MVD video sequences that the analog-based scheme offers a great advantage over the conventional digital-based scheme. For instance, the video quality of one intermediate virtual viewpoint is approximately 2.1 dB higher than digital-based schemes at a wireless channel quality, i.e., signal-to-noise ratio (SNR), of 15 dB.

Screen-camera communication, which uses a liquid crystal display (LCD) screen and camera image sensors, has been an attractive variant of visible light communications (VLC) since display and camera have been equipped with in various mobile devices. To improve transmission rates, we investigate the impact of nonlinear channel equalization and nonbinary channel coding as well as high-order modulation schemes. Equalization techniques can reduce the effect of channel impairments, such as color mixing. Nonbinary coding improves reliability of high-order modulation and thus increases the transmission rate. Experimental evaluations using an LCD screen and camera demonstrate that our proposed scheme achieves 3.6-2.4 times higher transmission rates compared to existing schemes for a communication distance of 40-100 cm.

Progressive download for multi-view video delivery is one of promising techniques to provide immersive and interactive experiences to users. In conventional multi-view video systems, a user downloads videos of all viewpoints of one content to realize smooth view switching. However, it causes increase of video traffic, and thus low video quality within an available download rate. To download only a desired viewpoint is another approach for reduction on volume of video traffic. However, playback stalls occur after view switching. The stalls induce low user's satisfaction for applications. In this paper, we aim at two objectives: 1) to achieve reduction on video traffic and 2) to achieve reduction on the number of playback stalls. To this end, we propose a new multi-view video delivery scheme for progressive download. The main idea of the proposed scheme is that the user only downloads potential videos, which are potentially played back by the user, to realize both traffic reduction and smooth view switching. In addition, we propose two download scheduling algorithms to prevent playback stalls even in a low download rate. The first algorithm prevents stalls in frequent view switching cases while the other prevents stalls in gazing cases. Evaluations using Joint Multi-view Video Coding (JMVC) encoder and multi-view video sequences show that the proposed method reduces video traffic by 55.1% compared with a simulcast scheme. In addition, the proposed method decreases the number of playback stalls during video playback by 86.0% compared with a request and response scheme.

Soft video delivery, i.e., analog video transmission, has been proposed to provide graceful video quality in unstable wireless channels. However, existing analog schemes need to transmit a significant amount of metadata to a receiver for power allocation and decoding operations. It causes large overheads and quality degradation because of rate and power losses. To reduce the overheads while keeping high video quality, we propose a new analog transmission scheme. Our scheme exploits a Gaussian Markov random field for modeling video sequences to significantly reduce the required amount of metadata, which are obtained by fitting into the Lorentzian function. Our scheme achieves not only reduced overhead but also improved video quality, by using the fitting function and parameters for metadata. Evaluations using several test video sequences demonstrate that our proposed scheme reduces overheads by 97 % with 3.4 dB improvement of video quality compared to the existing analog video transmission scheme.

The multi-view video streaming is one of promising technologies for emerging video services. For those applications of multi-view video streaming, video frames of all viewpoints, i.e., cameras, are needed to be transmitted to viewers because the demands of all the viewers' view-switching are unpredictable. However, existing transmission schemes are highly vulnerable to frame loss. Specifically, the frame loss in one viewpoint induces a collapse of decoding for other viewpoint videos. To improve loss-resilience, we propose a multi-path based multi-view video transmission. Our scheme encodes video frames into multiple descriptions that are mutually independent of each other, by using inter-view prediction. It then transmits each description using multiple transmission paths. Our scheme makes three contributions:1) it reduces video traffic even for a large number of cameras, 2) it prevents an increase in the number of undecoded video frames caused by one frame loss, and 3) it conceals frame loss by using the video frames in the other paths. Our scheme generalizes to an arbitrary number of transmission paths and discusses the detailed performance at the number of the paths of 2. Evaluations show that our proposed scheme improves video quality by 3 dB compared to existing transmission schemes in loss-prone environments.

The quality of wireless video delivery is susceptible to wireless channel instability, including channel fading, interference, noise, and packet loss. To improve the video quality in such wireless channels, analog transmission schemes have been proposed recently. However, the existing analog schemes have two drawbacks in high energy of video signals and loss resilience. To overcome the issues, we propose a new hybrid digital-analog transmission scheme, which jointly uses digital coding and analog coding based on compressive sensing. Digital coding generates the residual signals between the original and the encoded signals to decrease the energy of data for analog coding. Compressive sensing redistributes the energy across whole video packets to increase the resistance to packet loss. We show that our proposed hybrid scheme improves video quality by up to 11.6 dB compared to the existing analog scheme in an erasure wireless channel. In addition, our proposed scheme also improves video quality by 1.5 dB compared to the existing hybrid scheme.

Multi-view video system includes three sections: acquisition, transmission, and display. This paper focuses on the acquisition of multi-view video. Existing multi-view video acquisition studies exploit multi-camera arrays mutually connected by wires. However, this imposes the limitations of places and objects. To overcome the limitations, we exploit multiple mobile cameras and wireless networks for multi-view video acquisition. The acquisition of the multi-view video needs to achieve a reduction in video traffic while maintaining high video quality for communication between mobile cameras and an access point. This paper proposes Multi-view Video Streaming with Mobile Cameras (MVS/MC) to satisfy these requirements. MVS/MC has two features: packet overhearing and transmission order control. First, each mobile camera overhears other cameras' video packets, and encodes its own video frames using the overheard video packets. Second, the access point controls the transmission order of the mobile cameras, thus realizing bidirectional interview prediction. Bidirectional inter-view prediction exploits the inter-camera domain correlation among the mobile cameras to further remove the redundant information. Evaluations using multi-view video sequences show that, compared with existing methods, MVS/MC reduces the volume of traffic with only a slight degradation in video quality. For example, MVS/MC reduces traffic by 52 % compared to existing methods when PSNR is 36 dB.

Multi-view video consists of multiple video sequences captured simultaneously by multiple closely spaced cameras and enables users to freely change their viewpoints by playing different video sequences. Therefore, the transmission bitrate of multi-view video requires more bandwidth than conventional multimedia. In order to reduce the bandwidth requirement, User Dependent Multi-view Video Transmission (UDMVT), which is based on Multi-view Video Coding (MVC), has been proposed for single users. In UDMVT, the same frames are encoded into different versions for each user, which increases the transmission bitrate for multiple users because of the redundant transmission of overlapping frames. In order to address this problem, we proposed User dependent Multi-view video Streaming for Multi-users (UMSM). UMSM possesses two characteristics: overlapping frames required by multiple users are transmitted only once by multicast while un-overlapping frames required by each user are transmitted by unicast and offset of video requests between multiple users is aligned so as to maximize the area of overlapping frames. UMSM achieves a low transmission bitrate of multi-view video for multiple users by means of combining these characteristics. To investigate the effect of user's view switching on traffic reduction, we design three types of view-switching models, namely, scanning model, random model, and watching model. Simulation results obtained using benchmark test sequences provided by MERL reveal that UMSM decreases the transmission bitrate by 80.6% on average for five users, as compared to MVC, when users gaze a viewpoint for a while after switching views in order to search an attractive viewpoint.

Multi-view video consists of multiple video sequences captured simultaneously from different angles by multiple closely spaced cameras. It enables the users to freely change their viewpoints by playing different video sequences. Transmission of multi-view video requires more bandwidth than conventional multimedia. To reduce the bandwidth, UDMVT (User Dependent Multi-view Video Transmission) based on MVC (Multi-view Video Coding) has been proposed for single user. In UDMVT, for multiple users the same frames are encoded into different versions for each user, which increases the redundant transmission. For this problem, this paper proposes UMSM (User dependent Multi-view video Streaming for Multi-users). UMSM possesses two characteristics. The first characteristic is that the overlapped frames that are required by multiple users are transmitted only once using the multicast to avoid unnecessary duplication of transmission. The second characteristic is that a time lag of the video request by multiple users is adjusted to coincide with the next request. Simulation results using benchmark test sequences provided by MERL show that UMSM decreases the transmission bit-rate 55.3% on average for 5 users watching the same multi-view video as compared with UDMVT.