Satellite edge caching enables satellites to store and deliver content locally, offering a promising approach to support low-latency and on-demand video services. In this paper, we introduce the concept of Closer is not always better in ultra-dense Low Earth Orbit (LEO) constellations. This refers to scenarios where users are simultaneously covered by multiple satellites, while geographically closer satellites may appear more suitable for service delivery, they often face challenges in inter-satellite cooperation. To address this, we propose a hop-based cooperative satellite caching (HCSC) method. Specifically, we develop a hop-based fast visible satellite selection strategy to identify the top-k satellites for cooperative caching. Furthermore, we design a Dueling Deep Q-Network (Dueling DQN)-based algorithm to optimize caching decisions, aiming to minimize service delay through effective satellite collaboration. Simulation results demonstrate that the proposed HCSC method outperforms other baseline caching algorithms by reducing transmission delay and improving cache hit ratio.

The extensive coverage of satellite constellations has rendered the satellite–terrestrial integrated network (STIN) a pivotal solution for communication and computation services in internet of vehicles (IoVs) rescuing in remote or disaster areas with limited terrestrial networks. To optimise network resource utilisation and service quality, the integration of the service function chain (SFC) into STIN‐enabled IoV rescuing systems has become essential. However, traditional SFC‐based STIN systems encounter challenges in flow scheduling flexibility, stemming from the sequential execution of subtasks on satellites equipped with virtual network functions (VNFs). This leads to a trade‐off between data volume reduction and the additional communication and computation energy costs incurred in the orbit. To address this issue, this paper introduces a space ground collaborative SFC (SGC‐SFC) flow scheduling strategy. This strategy enables the execution of subtasks on either VNF‐equipped satellites or the ground vehicle formation, contingent on network conditions. Firstly, we carry out a computation–space–time graph (CSTG) model specifically for the STIN‐enabled IoV rescuing system with SFC. This model integrates the computational layer into the space–time graph (STG), accurately capturing the data volume reduction characteristics and sequential execution constraints of SFC in the STIN‐enabled IoV rescuing system. Secondly, a SGC‐SFC flow scheduling algorithm is designed to identify a set of feasible paths with minimal energy cost and maximum processable data volume. Simulation results validate the effectiveness and robustness of our proposed SGC‐SFC under diverse conditions.

With advancements of in‐vehicle computing and Multi‐access Edge Computing (MEC), the Internet of Vehicles (IoV) is increasingly capable of supporting Vehicle‐oriented Edge Intelligence (VEI) applications, such as autonomous driving and Intelligent Transportation Systems (ITSs). However, IoV systems that rely solely on vehicular sensors often encounter limitations in forecasting events beyond current roadways, which are critical for regional transportation management. Moreover, the inherent temporal dependency in VEI application data poses risks of interruptions, impeding the seamless tracking of incremental information. To address these challenges, this paper introduces a joint task offloading and resource allocation strategy within an MEC environment that collaboratively integrates vehicles and Roadside Infrastructure Sensors (RISs). The strategy carefully considers the Doppler shift from vehicle mobility and the Tolerance for Interruptions of Incremental Information (T3I) in VEI applications. We establish a decision‐making framework that actively balances delay, energy consumption, and the T3I metric by formulating the task offloading as a stochastic network optimization problem. Utilizing Lyapunov optimization, we dissect this complex problem into three targeted subproblems that include optimizing local computational capacity, MEC computational capacity and comprehensive offloading decisions. To tackle the efficient offloading, we develop algorithms that separately optimize offloading scheduling, channel allocation and transmission power control. Notably, we incorporate a Potential Minimum Point (PMP) algorithm to boost parallel processing and simplify computational scale through matrix decomposition. Evaluations of our algorithm show that it excels in both complexity and accuracy, with accuracy improvements ranging from 74.3% to 114.0% in asymmetric resource environments. Simulation and experimental studies on offloading performance validate the effectiveness of our framework, which significantly balances network performance, reduces latency, and improves system stability.

Mars exploration highlights the demand for identifying Martian surface changes, which has sparked research interests in planetary surface changes detection (PSCD). However, the prevailing PSCD algorithms face significant challenges due to the sparse features, low resolution, and high noise levels of captured images data. In this paper, we propose an unsupervised model, the Dust Unsupervised Surface Tracking Network (DUSTNet), designed to track the surface changes caused by Martian dust storms. Our DUSTNet employs a network architecture with dual input branches to learn the cross-temporal complementary information from pre-time and post-time image pairs. A multi-level feature complementary fusion (MFCF) module is utilized to enhance the ability to detect subtle changes. Considering the difficulties in image registration caused by illumination variations, noise, and other factors, we design a noise-resistant module that mitigates pseudo-changes and improves the robustness of PSCD. In addition, we construct a dataset of Martian dust storms change detection based on the images captured by Moderate Resolution Imaging Camera (MoRIC) of China’s First Mars Mission TianWen-1 (The dataset is available at https://github.com/Limiyu1123/SDS). The detection performance of DUSTNet on multiple Mars surface datasets, including our Martian dust storm test set. Our model achieves improvements of 2.5% in precision, 7.55% in F1-score, 6.54% in OA, and 4.57% in Kappa over the state-of-the-art model.

Multispectral (MS) images of Mars are extensively used for material recognition and classification. However, due to constraints like limited lighting, photon issues, and atmospheric interference, these images inevitably suffer from noise, which greatly hinders their further applications. The scarcity of MS images and limited research on their inherent noise characteristics make denoising a more challenging task. In this paper, we employ a novel unsupervised estimation technique to quantify noise, which does not depend on prior knowledge of distributions, thereby enabling implicit modeling of the noise. By integrating the quantified noise characteristics into denosing model with attention mechanism, the model’s ability to perceive and adapt to noise is significantly enhanced. Experiment shows that our method not only proves effective in denoising raw Mars MS images but also demonstrates competitive performance compared to state-of-the-art methods.

Cybertwin-enabled 6G, by mapping physical entities to Cybertwins endowed with distributed data-centered autonomy, is envisioned to cater to intelligent and flexible services. It plays a critical role in resolving potential conflicts in service integration arising from the proliferation of Internet of Everything devices (IoEDs). However, Cybertwins may encounter temporal dependencies when decoupling data and functionality during the offloading process. To address this issue, we initially decouple the offloading into three segments, adaptively distributed among IoEDs, Cybertwins, and the edge cloud. Subsequently, the Potential Minimum Point with Temporal Lag Tolerance (PMP-TLT) algorithm is introduced. The proposed PMP-TLT utilizes the Temporal Lag Tolerance (TLT) between Cybertwins and IoEDs, breaking the Cybertwins into collaborative active and dormant states. This separation facilitates parallel processing, boosting computational speed. Finally, we highlight the merits of our algorithm in terms of efficiency and complexity, especially for asymmetric solving scales. Additionally, our analysis underscores the performance benefits of the Cybertwin-enabled 6G design, particularly regarding power savings and reduced latency.

Low Earth Orbit (LEO) Mega-Constellation Net-works (MCNs) have become increasingly popular in recent years due to their ability to offer global coverage, high-throughput data transmission, and low latency for users across the world. While MCNs significantly improve continuous service coverage worldwide, they also pose a challenge in terms of managing a large number of satellites in the system, which increases the complexity of routing and the number of hops required for inter-satellite links (ISLs). In this paper, we propose a software-defined mega-constellation network (SDMCN) architecture that provides flexible network management by enabling dynamic global network monitoring and collecting satellite information. In order to optimize routing in Walker Delta MCNs, we propose an orbit-grid-based dynamic routing (OGDR) algorithm, which begins by developing a path model that selects access satellites based on both the minimum hop metric and the ISLs distribution. Then, leveraging the periodicity of the satellite and the characteristics of the path model, inter-plane and intra-plane links are selected on an orbit grid directly based on the latitude of the satellites to ensure the minimum delay. Finally, we introduce a path failure recovery mechanism to ensure the reliability of the path. Experimental results demonstrate that the algorithm not only achieves performance similar to the shortest path but also minimizes the number of ISL hops and has lower computational complexity.

Satellite-terrestrial integrated network (STIN) possesses the advantage of offering ubiquitous coverage, thereby enabling people to access service freely even in remote areas. There is a growing market for immersive media (IM), and delivering high-quality IM services via stable routes has become a highly active research area. However, existing routing approaches primarily focus on optimizing network performance, the tailored routing strategies for IM services are scarce in the literature. In this paper, we propose an economic routing (EcoR) method for IM services in STIN. Specifically, for the scenario where IM service requests are highly concentrated, utilizing the cost balance method to lighten the burden of maintaining heavy flows on satellite networks. We formulate an optimization problem that aims to direct the traffic to less congested links while ensuring an IM service guarantee and achieving a cost balance for the network. To this end, we propose an advanced Dijkstra algorithm that combines the deep forest searching method and the traditional Dijkstra algorithm to identify all potential routing paths. Additionally, we adopt a greedy approach to select the most appropriate routing path that minimizes the cost. Evaluation results show that the proposed EcoR algorithm achieves cost savings of 32% and a service success rate increase of 64% when compared to other routing methods.

With the rich 4D visual information of light rays, light field (LF) applications contribute to the development of immersive multimedia and virtual reality, where LF depth estimation is the critical problem. However, existing light field depth estimation algorithms deliver weak performance in edge and textureless regions. In this paper, we propose the feature-extraction and attention mechanism-based network (FEAMNet), which can effectively handle edges and textureless regions in depth maps. The FEAMNet contains a dilated-convolution and average-pooling (DCAP) feature extraction module with a large receptive field to acquire multi-scale features. And the channel attention-based disparity regression (CADR) module is introduced to measure the importance weights of different feature channels for high accuracy. The experimental results show that the FEAMNet outperforms state-of-the-art algorithms, such as OACC and DistgDisp. The implementation of our FEAMNet with the mentioned dataset is open sourced at https://github.com/lymwxq/FEAMNet.

Viewport prediction is a critical aspect of virtual reality (VR) video streaming, directly impacting user experience in adaptive streaming. However, most existing algorithms treat users as homogeneous entities and overlook the variations in user behaviors and video content. Additionally, they often struggle with long-term predictions and intense movement. Our research sheds light on the importance of considering user behavior variations and leveraging advanced techniques to optimize robust viewport prediction in VR video streaming. First, we address these limitations by conducting a comprehensive feature analysis on existing datasets to uncover distinctive user behaviors. Building upon these findings, we propose a novel approach that utilizes the power of Bidirectional Long Short-Term Memory (BiLSTM) networks and meta-learning. The BiLSTM architecture effectively captures long-term dependencies, which can strengthen the robustness of viewport prediction especially in longterm prediction and intense movement. Additionally, meta-learning enables personalized adaptation to individual users’ viewing behaviors. Through extensive evaluations on diverse datasets, our algorithm Meta360 demonstrates superior performance in terms of accuracy and robustness compared to state-of-the-art methods.

The Mars exploration mission of China named Tianwen-1 is being carried out as scheduled. The Navigation and Terrain Cameras (NaTeCam) equipped on the Zhurong Rover play an essential role in obstacle recognition. The main obstacles on the Martian surface are rocks of different sizes, which influence the path planning of Zhurong Rover in scientific exploration. Most existing semantic segmentation methods are based on the U-Net architecture with ResNet or other backbones, and features extracted by these methods lack long-range dependencies. To fully exploit the context information, we propose the MarsNet framework for the Mars image, which combines transformers with the convolutional neural network (CNN) as the backbone, and hybrid dilated convolution (HDC) is also employed to the decoder path to help detect the huge rocks. Besides, since there are few open-source datasets for rock segmentation for Mars, we establish a segmentation dataset from the Martian surface image, named TWMARS, captured by NaTeCam. Extensive experiments are conducted on the TWMARS dataset, and the experimental results demonstrate that MarsNet achieves accurate rock segmentation and outperforms state-of-the-art methods. The source code is available athttps://github.com/BUPT-ANT-1007/MarsNet.

In software-defined networks, due to the limited flow table capacity, unreasonable rule placement will cause the flow table overflow problem. These flows without flow rules installed need to be processed by controller, which increases and even unbalances controller load. Based on the average packet end-to-end delay, we propose a rule placement and switch migration-based scheme for controller load balancing. In the routing and rule placement phase, the Cost-Aware Routing (CAR) algorithm takes into account the flow table occupancy while utilizing the installed rules to alleviate flow table overflow and preliminarily balance the controller load. In the switch migration phase, the Benefit-Cost Switch Migration (BCSM) algorithm obtains the migration option with the maximum total benefit. Numerical results show that the CAR algorithm reduces and balances controller load to achieve lower delay than Random and FlowStat. And the BCSM algorithm balances the controller load and reduces packet delay than SMCS and ESMLB.

Light field records the information of the light in space and contributes to regenerate the content effectively, which makes immersive media more promising. In this paper, we propose a geometry-guided compact compression scheme (GCC) for light field image. We regard that the geometry of GCC includes the structure in a single sub-aperture image (SAI) and the relationship among SAIs, which can be used to fully explore the compact representation for light field image. The light field image is grouped into key SAIs and non-key SAIs. The key SAIs are obtained by down-sampling in the angular domain and arranged into the pseudo-sequence that needs to be compressed. We consider the superpixel-based segmentation algorithm to detect the contours and obtain the sketch map for the non-key SAIs. Meanwhile, the graph model is used to establish the relationships among the SAIs by the vertices and edges. On the decoder side, the light field image is reconstructed by the graph convolutional networks, and the sketch map optimizes the details of the recovered images to some extent. Experimental results show the benefit of GCC in terms of rate-distortion performances compare with several state-of-the-art methods for the real-world and synthetic light field datasets. Besides, the proposed GCC is able to generalize over datasets not seen during training.

As a kind of medium that provides strongly immersive experience, 360° videos suffer greatly from pixel inefficiency as the content will not be fully viewed by users, leading to a high-bandwidth requirement of streaming. Recently, Tile-based streaming systems have become popular to lower bandwidth usage. However, most of these systems inevitably treat non-viewport areas as viewport because the fixed tiling configuration fails to adapt to the viewport effectively. A finer-grained tiling configuration helps adapt to the viewport, but also introduces significant encoding overhead. Recently proposed dynamic tiling systems address the issue by tiling chunks dynamically based on the features of projected 360° videos. However, because projection inherently introduces serious distortion to image, the results can be misleading. To overcome the viewport adaption problem, we propose Spherical Viewport-Aware Dynamic Tiling for 360° Video Streaming (SAD360). Given that popularity of different areas can be reflected by viewers' behaviours on the whole, a dynamic tiling algorithm is proposed to find the optimal tiling configuration for each chunk by analysing head movement data in hand on a sphere. The algorithm tries its best to generate bigger tiles to reduce encoding overhead and still manages to adapt to the viewport effectively. We also use Reinforcement Learning (RL) to solve the problem of bitrate allocation of tiles varying in size. Experiments demonstrate that our system can get a 14% average QoE gain compared with fixed tiling configuration.

Upsampling of 3D point clouds plays an important role in point cloud reconstruction, rendering, meshing and analysis. Most of the existing point cloud upsampling networks are three-tier cascaded networks that use a combination of feature extraction, feature expansion, and coordinate reconstruction. However, for the point clouds reconstructed by this network architecture, there is a non-negligible deviation between the point clouds after upsampling and the ground truth, especially in the details. In this paper, we propose a four-tier cascaded graph convolutional network architecture called Refine-PU, which di-vides the network into four parts, i.e., feature extraction, feature expansion, coordinate reconstruction, and spatial refinement. We first design a multi-scale graph convolutional feature extractor called Dense Edge Conv (DEC) to better capture the global and the local structural features of point clouds. In addition, the network appends a spatial refinement module on the basis of the traditional three-tier cascaded network, in order to further adjust the details of the coarse dense point clouds obtained after upsampling to make them more consistent with the ground truth. Extensive experiments implemented on both synthetic and real-scanned datasets demonstrate the superiority of our method over the state-of-the-art methods both quantitatively and qualitatively.

In China’s Tianwen-1 mission for Mars exploration, Zhurong rover carries the Navigation and Terrain Camera (NaTeCam), and collects a lot of terrains and topographic data. For the scientific popularization of Mars, a virtual reality roaming based on panoramic stitching presents the Martian surface in detail and provides an immersive experience for users. However, the current Mars roaming systems focus on global information instead of details, and the traditional panoramic stitching methods are not suitable for Mars images. This letter proposes a panoramic stitching method and develops a 3DoF+ Mars roaming system. The proposed Stitching-Combines-Features-and-Projection (SCFP) jointly considers the extracted features of images and projection based on intrinsic camera parameters, which improves the matching effect and execution efficiency. Experimental results indicate that SCFP reduces the computational time and improves stitching results than state-of-the-art methods. Further, we design and deploy the 3DoF+ roaming system based on panoramic stitching with datasets obtained from the Tianwen-1 mission.

With the increasing demand of virtual reality (VR) video applications, it is necessary to adopt corresponding strategies to deal with the challenges they bring to the network. Multipath routing is proposed to address the VR video bandwidth problem by splitting a large flow into multiple subflows and routing them separately. In addition, Software Defined Networking (SDN) is used to manage these subflows so that they are assigned to the appropriate paths. This paper presents a MCTS-based VR video multipath transmission approach (MVRMPT), which allocates better paths to the VR video tiles that have greater impacts on the user’s Quality of Experience (QoE). More specifically, the Monte Carlo tree search (MCTS) algorithm is modified to find multiple disjoint paths with the minimum delay between node pairs. Then the paths are sorted by the predicted QoE. Finally, the VR video is spatially divided into different zones, and these zones are assigned to different paths according to their impacts on the user’s QoE. The proposed algorithm is implemented in the SDN controller, and the evaluation results show that our method achieves higher QoE and network throughput.

We present a 4D Light Field (LF) video dataset, collected by a custom-made camera matrix, to be used for designing and testing algorithms and systems for LF video coding, processing, and streaming. Compared to existing LF datasets, ours provides LF videos, as opposed to only images, and at higher frame resolution, higher number of viewpoints, and/or higher framerate, offering the best visual quality LF video dataset. To achieve this, we built a 10 x 10 LF capture matrix composed of 100 cameras, each with a 1920 x 1056 resolution. We used this matrix to record videos in real and varying illumination and scene dynamics conditions. The dataset contains a total of nine groups of LF videos: eight groups collected with a fixed camera matrix position and orientation recording indoor potted plants, furniture, etc., and the last group collected by rotating around an outdoor environment with roadside vehicles, pedestrians, etc. Each group of LF videos consists of 100 video streams encoded with H.265/HEVC. Scene changes vary from static to slightly dynamic to highly dynamic, providing a good level of diversity. As an example, we present the results of a depth estimation method and show that our dataset can be used for applications such as objection detection, 3D modeling, and others.

As an emerging media format, virtual reality (VR) has attracted the attention of researchers. 6-DoF VR can reconstruct the surrounding environment with the help of the depth information of the scene, so as to provide users with immersive experience. However, due to the lack of depth information in panoramic image, it is still a challenge to convert panorama to 6-DOF VR. In this paper, we propose a new depth estimation method SPCNet based on spherical convolution to solve the problem of depth information restoration of panoramic image. Particularly, spherical convolution is introduced to improve depth estimation accuracy by reducing distortion, which is attributed to Equi-Rectangular Projection (ERP). The experimental results show that many indicators of SPCNet are better than other advanced networks. For example, RMSE is 0.419 lower than UResNet. Moreover, the threshold accuracy of depth estimation has also been improved.

In order to meet the Quality of Experience (QoE) requirements of Virtual Reality (VR) video users under limited resources, efficient and adaptive routing scheme is required. The next generation mobile networks 5G can match network and computing resources according to service requirements, which will be the communication technology for the VR industry. In 5G architecture, the introduction of Software Defined Networking (SDN) decouples the control plane and the forwarding plane, and provides the ability of more granular network resource management. It can actively allocate resources for VR video to optimize transmission performance. In this paper, a QoE-driven Fine-grained routing (QFR) scheme based on SDN has been proposed. The core of QFR is the route calculation algorithm and the route allocation strategy. The route calculation algorithm is a two-stage adaptive routing algorithm. In the first stage, by means of an improved Dijkstra algorithm, the algorithm calculates k paths with the shortest delay. In the second stage, the k paths with the shortest delay are ranked according to the predicted QoE of each path. In addition, tile-based VR video provides a prerequisite for fine-grained routing scheduling. Through differentiated routing of Field of View (FoV) video streaming and Non-FoV video streaming, we develope a fine-grained route allocation strategy. The route allocation strategy determines how to allocate the sorted k paths with the shortest delay according to the residual bandwidth. Comparative evaluation of QFR is conducted to show its preponderance over several existing routing schemes, in terms of download bitrate and QoE of VR video.

As a new form of volumetric media, Light Field (LF) can provide users with a true six degrees of freedom immersive experience because LF captures the scene with photo-realism, including aperture-limited changes in viewpoint. But uncompressed LF data is too large for network transmission, which is the reason why LF compression has become an important research topic. One of the more recent approaches for LF compression is to reduce the angular resolution of the input LF during compression and to use LF reconstruction to recover the discarded viewpoints during decompression. Following this approach, we propose a new LF reconstruction algorithm based on Graph Neural Networks; we show that it can achieve higher compression and better quality compared to existing reconstruction methods, although suffering from the same problem as those methods—the inability to deal effectively with high-frequency image components. To solve this problem, we propose an adaptive two-layer compression architecture that separates high-frequency and low-frequency components and compresses each with a different strategy so that the performance can become robust and controllable. Experiments with multiple datasets 1 show that our proposed scheme is capable of providing a decompression quality of above 40 dB, and can significantly improve compression efficiency compared with similar LF reconstruction schemes.

As a new form of volumetric media, the technology of light field and its compression has gradually become the research hotspots in academia. The scheme of compressing using the sparsity of the light field is a very promising idea, which has the characteristics of high compression rate and is not affected by the occlusion of scene objects. However, the instability of the reconstruction algorithm's performance on different datasets limits the further application of this solution. Since the quality of the decompression outputs will be limited below the reconstruction result, the poor performance of the reconstruction algorithm on some light field images will result in a very low PSNR upper limit for the compression scheme. This paper finds that the main reason for this performance problem is the poor ability of the algorithm to process the high-frequency components of the light field. And in order to solve it, an adaptive two-layer light field compression scheme is presented. The proposed scheme separates the high-frequency components and the low-frequency components of the light field so that they can be independently compressed. Through the adaptive adjustment, the data of different frequency component can adopt different compression strategies, so that the performance of the proposed scheme can be optimal. Experiments with multiple datasets1 show that the proposed scheme can break the upper limit of PSNR caused by sparse reconstruction and is capable to provide decompression results above 40 dB. It also achieves significant improvement in compression efficiency under diverse requirements.

The light field records all the information about the light in space. With this property, it has a very good application prospect in immersive media. Due to the large amount of light field data, light field compression technology has attracted much attention. The graph data structure of vertices and edges can well describe the relationship between the light field viewpoints, which is suitable for processing light field data. At the same time, the graph convolution network (GCN) combines graph and neural networks and has great potential for processing graph data. In this work, a light field compression scheme based on the graph convolutional network has been proposed. On the compression side, the scheme will select the anchor views and the entire light field as the input and label of the GCN, which generates a network model. This model describes the relationship between the light field viewpoints. On the decompression side, the scheme reconstructs the entire light field using the model and the extracted anchor views. Experimental results show that the proposed scheme has superior performance in reconstructed images. Compared with state-of-the-art, it can achieve a gain of up to 4 dB on PSNR. Besides, the scheme has been proved to have good generalization capability.

The light field has excellent application prospects in immersive media because of the abundant information of the light. Due to the sparsity and redundancy in light field images, light field reconstruction based on compressed sensing is used to recover light field images from only a few measurements. And the light field compressed sensing usually optimizes the measurement matrix and the dictionary and processes each of the light field images separately. Since the high similarity of light field images, the different viewpoints of images can be stacked together and formed as a 4D tensor. In this paper, we propose tensor based on compressed sensing (TCS) method to yield measurements with common characteristics. Besides, a better deep learning network is designed for TCS, the measurement matrix optimization and image reconstruction will be performed simultaneously. Experimental results show that the proposed method gets at least 3 dB gain in PSNR and outperforms state-of-the-art in the reconstruction quality.

Compressed sensing permits sub-Nyquist sampling, breaking the bottleneck of traditional Nyquist theory. The random pulse-position-modulation analog-to-digital converter (PPM ADC) is a sub-sampling structure, employing compressed sensing theory. To reconstruct signal in PPM ADC, period random sampling reconstruction (PRSreco) algorithm is firstly used, but it requires signal's prior sparsity information. Iterative support detection (ISD) algorithm recovers signal with no need of sparsity, thus we apply it to PPM ADC. To obtain a more accurate recovery especially when sub-sampling ratios are low, we propose an improved-ISD algorithm in this paper. Based on ISD, we add a SupportCorrection module and a denoising module. The former is implemented to choose more appropriate frequency support and the latter is carried out to eliminate the burr of frequency coefficients. Simulation results demonstrate that improved-ISD increases the recovery robustness at low sub-sampling ratios and augments mean output SNR.

Analog transmission schemes for wireless video multicast, especially SoftCast, have gained great attention recently. SoftCast can overcome the “cliff effect” by delivering the linear transformations of video pixels, which is unavoidable for digital schemes. Considering the fact that different parts of image/video have diverse importances to the human perception, in this paper, a new analog video multicast scheme named SCAST is proposed. SCAST is based on SoftCast and the image segmentation technology, designed to improve the video subjective visual quality. Firstly, SCAST employs the Otsu segmentation algorithm to decompose the video source into the foreground part and the background part. Then a power allocation method is presented to provide strong protection for the foreground part, which is the region of interest (ROI) of video for the human visual system (HVS). The two parts are both encoded in the analog way by SoftCast and mapped into a complex signal before transmitted. Results show that the proposed scheme SCAST outperforms SoftCast, H.264/AVC and WSVC in both the metrics, i.e., peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), which reflect the objective and subjective video qualities separately. Especially, SCAST performs excellently under bad channel condition.

Multiband signal is an important signal in radar and communication systems, which is generally sparse and with a wide spectrum. Due to the wide spectral range of multiband signals, traditional ADC sampling at Nyquist rate may be too difficult to achieve. Modulated wideband converter (MWC), which is based on compressed sensing (CS), is a subtle scheme to solve wide spectrum sampling problem. In addition, reconstruction algorithm is the key of MWC which can decide the performance of the architecture. In this paper, we propose an improved reconstruction method, which expands iterative support detection (ISD), an iterative recovery algorithm for one- dimensional signal, to continue to finite (CTF) system. Our algorithm uses ISD to compute the solutions which satisfy that every column's reconstructions is l1minimization. Then, utilizing the resulting matrix and support detection rule, we can find the support set and obtain the recovered signal. Our algorithm does not require the number of support set as prior knowledge. And the property of our method is close to orthogonal matching pursuit multiple measurement vectors (OMPMMV), and better than Sparsity adaptive matching pursuit multiple measurement vectors (SAMPMMV). Simulation results confirm the conclusion.

Hybrid Digital-Analog (HDA) transmission for wireless video multicast, has gained great attention recently, as it can integrate the advantages of digital and analog coding simultaneously. In this paper, a new HDA video transmission scheme is proposed, named VCAST, which exploits the visual information to improve both subjective visual and objective quality. Since the visual information reflects human perception and mainly exists in middle and high frequency bands, VCAST extracts these bands of video and transmits them in digital way, i.e., H.264/AVC, for high transmission accuracy and good subjective quality. Then the remaining of the video, namely the insensitive information, is processed by 2D-DWT and divided into the approximation and the details parts, i.e., LL band and HL, LH, HH bands. The LL band is also transmitted by H.264/AVC. The HL, LH, HH bands and the residual of LL band are transmitted in analog way by SoftCast, for saving energy and good objective quality. Results show that the proposed VCAST significantly outperforms WSVC, SoftCast and H.264/AVC in both the objective metric (i.e., peak signal-to-noise ratio, PSNR) and the subjective metric (i.e., structural similarity, SSIM).

Wireless video has almost become an indispensable part of daily life. Conventional wireless video systems which contain separate source coding and channel coding are not suitable for broadcasting scenarios because of the rapid changes of channel status. The newly presented SoftCast is a jointly source-channel coding scheme which provides graceful quality transition but is not very efficient in important data protection due to operation over chunk. And the important data mainly exist in low frequency band. In this paper, we propose a Scalable Wireless video broadcast scheme based on Unequal Protection (SWUP) which allocates more bandwidth and power to important data for better accuracy. We use a method, named pseudo coset coding, to divide the important data into two parts by using the selected quantization step, where the coset index data are transmitted using conventional digital framework, while the residual codes utilizes SoftCast way. The simulation results show that our proposed scheme is about 3 ~ 5dB and 2 ~ 4dB better than 3D SoftCast and WaveCast in reconstruction quality respectively.

In wireless video broadcast system, analog joint source-channel coding (JSCC) has shown advantage compared to conventional separate digital source/channel coding in the aspect that it can avoid cliff effect gracefully. What's more, analog JSCC only needs a little calculations at the encoder and has strong adaptability to different channel condition, which is very suitable to the wireless cooperative scenario. Thus in this paper, we propose a distributed cooperative video coding (DCVC) scheme for wireless video broadcast system. The scheme is based on the transmission structure of Softcast and borrows the basic idea of distributed video coding. Different from the former cooperative video delivery methods, DCVC utilizes analog coding and coset coding to avoid cliff effect and to make the best of transmission power. The experimental results show that DCVC outperforms the conventional WSVC and H.264/SVC cooperative schemes, especially when the cooperative channel is worse than the original source-terminal channel.

With the widespread popularity of mobile terminals, wireless video has become an indispensable part of daily life. The conventional wireless video transmission scheme which consists of separate digital source coding and digital channel coding is now unable to meet the broadcast and mobile scenarios owing to the dramatic changes in its channel conditions. However, a newly uncoded transmission scheme called SoftCast was presented to provide graceful quality transition. SoftCast performs power allocation over chunks to decrease the amount of Meta data. But the magnitudes vary dramatically in some chunks and SoftCast expends very large power to transmit some coefficients with large value. This paper introduces a scalable video SoftCast using Magnitude Shift to constraint the value of DCT coefficients. It only costs little power and bandwidth to transmit the overhead generated by Magnitude Shift. The experimental results show that the proposed scheme can improve the performance of the original SoftCast using 3D-DCT transform by 2-5dB.

In distributed multiview video coding (DMVC), the quality of side information (SI) is crucial for decoding and the reconstruction of the Wyner-Ziv (WZ) frames. Generally, its quality is influenced by two main reasons. One reason is that the moving object of the WZ frames can be easily misestimated because of fast motion. The other is that the background around the moving object is also easily misestimated because of occlusion. According to these reasons, a novel SI fusion method is proposed which exploits different schemes to reconstruct different parts complementarity. Motion detection is performed to extract the moving object which can be predicted by utilizing both temporary correlations and spatial correlations. As for background around the moving object, temporary correlations are utilized to predict it. It is noteworthy that the prediction method used in this paper is based on a feature based global motion model. The experiment results show high precision quality of the SI of the WZ frames and significant improvement in rate distortion (RD) performance especially for the sequence with fast moving objects.

The performance of the mobile video broadcast is weakened when applying the traditional broadcast scheme which is forced to pick a single bit rate supported by the worst receiver. It leads to the drawback that receivers with better channels cannot obtain better video qualities. This paper proposed distributed Realcast, an analog video delivery scheme based on real value transmission, to provide differentiated video service. It utilizes a series of linear operations to encode video without digital processing so that the channel noise is proportional to the distortion in video pixels and achieve channel adaptive transmission. Meanwhile, a distributed video coding scheme combining the frame difference method and the coset coding is proposed to realize the efficient video compression and prevent error propagation. In addition, distributed Realcast performs the motion compensated extrapolation method at both the encoder and decoder to avoid the transmission of motion vectors and release the stress of limited bandwidth. Simulation results indicate that distributed realcast outperforms Softcast, a representative analog video broadcast scheme, by nearly 3db.

With the deployment of the fourth generation communication networks, mobile video broadcast services are becoming one of the essential parts of users' daily lives. As an efficient way for mobile video transmission, digital broadcast scheme is faced with the challenge of cliff effect. A novel real-value scheme called SoftCast can eliminate the cliff effect, but not efficient in removing inter- frame correlation. The proposed scheme is similar to SoftCast, and to fully exploit the inter-frame redundancy, motion alignment is involved in the temporal wavelet transform. Then the dense constellation mapping of 64K quadrature amplitude modulation is utilized to maintain the real-value property for wireless transmission. Moreover, metadata is extracted from the previous processes and transmitted in the conventional mechanism to facilitate decoding. Simulation results verify the scalability and robustness of the proposed scheme in the mobile video broadcast applications.

To meet the requirement of very-high-rate-data transmission over wide bandwidths, carrier aggregation (CA) has been regarded as an important technology for Long Term Evolution-Advanced (LTE-A). It considers both primary component carrier (PCell) and secondary component carrier (SCell) and many operations are based on the PCell. In this paper, the load balancing performance of dynamic SCell measurement period relaxing in the LTE-A system with CA has been discussed. Furthermore, it gives the result of SCell measurement period relaxing window under different user equipment speed which is both energy-saving and harmless to the system performance.

To meet the requirement of very-high-rate-data transmission over wide bandwidths, carrier aggregation (CA) has been regarded as an important technology for Long Term Evolution-Advanced (LTE-A). It considers both primary component carrier (PCell) and secondary component carrier (SCell) and many operations are based on the PCell. In this paper, the load balancing performance of dynamic SCell measurement period relaxing in the LTE-A system with CA has been discussed. Furthermore, it gives the result of SCell measurement period relaxing window under different user equipment speed.

Scalable video coding technologies provide adaptive video applications in heterogeneous and polytropical conditions. However, the highly hierarchical feature makes the loss or unsuccessful recovery of the base-layer to be catastrophic. In this paper, a distributed scalable video coding scheme using the new advances of compressed sensing is proposed to solve this problem at the energy-constrained encoder. The wavelet coefficients of the video frames have inherent fidelity scalability which is utilized in our scheme. Furthermore, the democracy of measurements reduces the risk of base-layer loss over the packet loss channel. Experimental results show that our scheme outperforms the existing scalable compressed sensing scheme by about 2dB.

Handover Parameter Optimization (HPO) and Load Balancing (LB) are two Self-Organization network (SON) aspects which aim at improving LTE system handover performance and user's satisfaction respectively. However, there is often counteraction between LB and HPO, because LB would increase the frequency of inter-cell handover and correspondingly increase the possibility of handover problems. Furthermore, most of the LB and HPO jointly optimization methods don't consider the network allowed maximum radio link failure (RLF) ratio, which would increase the possibility of call dropping although the cell loading is balanced. In this paper we introduce the network allowed maximum RLF ratio as a key indicator and a dynamic hysteresis-adjusting (DHA) method to harmonize the two aspects. Furthermore, we take the realistic network situations into account to obtain a more reliable result. The proposed method is evaluated by a series of system-level simulation which witnesses an improvement in handover performance and number of satisfied users in LTE networks.

High Speed Railway(HSR) scenario was currently agreed as the main scenario in the 3GPP Rel.11 study item, Mobile Relay for E-UTRA. Usually, communications of high-speed railway systems suffer from problems such as Doppler spread, radio condition abrupt change and handover failure. Mobile relay is a promising scheme to solve these problems, but the quantitative performance improvement to HSR has not been fully evaluated and analyzed. In this paper, a high speed scenario with mobile relay integrated is presented to analyze these issues for LTE Advanced system. The proposed mobile relay solution with group mobility is evaluated by a series of system simulation which witnesses an improvement in train user throughput as well as system throughput, and higher handover success ratio with a decrease in radio link failure ratio.

The max C/I, Round Robin (RR) and Proportion Fair (PF) are three primary scheduling schemes adopted by LTE system to allocate shared resources among users in the time-frequency domain. However, most of the scheduling methods are static and ignore the relation between user dispersion degree of the sector and maximum PRB number (MaxPRB) allocated to one user. In this paper, we propose an improved dynamic MaxPRB-adjusting scheduling scheme (DDS) based on user SINR dispersion degree, which can win better tradeoff between throughput and user fairness in LTE downlink. Simulation results show that the proposed scheme will adjust the MaxPRB according to user SINR dispersion degree of the sector to obtain a good balance between sector throughput and user fairness.

Distributed compressive video sensing (DCVS), aiming at capturing and compressing video data simultaneously, is an emerging field which exploits both intra- and inter-frame correlation. In this paper, we present a new algorithm based on smoothed ℓ0norm (SL0) which tries to directly minimize the ℓ0norm to decode a Wyner-Ziv frame when parts of its correlated key frame's support is known as side information (SI) in a typical DCVS scenario. With the assistance of the modified initialization, our proposed algorithm can reconstruct the Wyner-Ziv frame of the same accuracy with much lower measurement rate compared to the case when the partially known support is not used as SI. It is experimentally shown that our proposed scheme outperforms GPSR at the expense of a tolerable decoding complexity. When compared with modified-cs, a large saving in decoding CPU time is achieved in sacrifice of some PSNR performance.

Adaptive modulation selection plays an important role in wireless communication and wireless sensor network since the wireless channel condition is time-varying due to some factors e.g. path-loss and multipath propagation. Therefore, using one modulation type cannot fulfill all the channel conditions and receivers. Conventional adaptive modulation selection schemes are based on packet loss statistics or SNR measurement. However, all of these methods are indirect approaches because they utilize the packet loss rate or SNR information to estimate the BER approximately and the modulation selection that based on these approximate results is not exactly. Therefore, in this paper, we propose to utilize the error estimating code (EEC) which directly represents the BER information to realize a new adaptive modulation selection scheme. Compared to traditional adaptive modulation type selection schemes, our scheme needs only a small amount of overhead and very little computational cost for the same performance.

Distributed Compressed Video Sensing (DCVS) has developed as one of the efficient solutions that guarantee low complexity video compression. In this paper, a novel DCVS algorithm with unequal protection of the video signal's elements is proposed. The new algorithm utilizes not only the sparsity and probability distribution of the video signal but also its particular unequal significance feature. Based on this feature, we design the structured irregular low-density sensing matrix to sample the signal. From the analysis and simulation results, it is confirmed that our method has higher recovery quality than the conventional Bayesian Compressed Sensing (CS) using Belief Propagation (BP). Moreover, the excellent noise-resilience of BP is preserved in our algorithm comparing to the DCVS schemes using optimization recovery.

In this paper, a local SNR aided sensor selecting (LSAS) algorithm is proposed to meet the energy efficiency requirement of wireless sensor networks (WSNs). In the conventional cluster routing protocols, during each round, all the subordinate sensors need to send their sensed information to their cluster heads, which is energy consuming. Realizing that it is not necessary to involve all the sensors due to the redundancy characteristic of the information on them, we propose that only those sensors with higher local SNR are selected to transmit their sensed data. Experimental results demonstrate that it is sufficient to only use the information on nodes with higher local SNR for target state estimation. The simulations also suggest that the proposed method consumes about 30%-50% less energy than the conventional method.