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.

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.

As a face manipulation technique, the misuse of Deepfakes poses potential threats to the state, society, and individuals. Several countermeasures have been proposed to reduce the negative effects produced by Deepfakes. Current detection methods achieve satisfactory performance in dealing with uncompressed videos. However, videos are generally compressed when spread over social networks because of limited bandwidth and storage space, which generates compression artifacts and the detection performance inevitably decreases. Hence, how to effectively identify compressed Deepfake videos over social networks becomes a significant problem in video forensics. In this paper, we propose a facial-muscle-motions-based (FAMM) framework to solve the problem of compressed Deepfake video detection. Specifically, we first locate faces from consecutive frames and extract landmarks from the face images. Then, continuous facial landmarks are utilized to construct facial muscle motion features by modeling the five sensory and face regions. Finally, we fuse the diverse forensic knowledge using Dempster-Shafer theory and provide the final detection results. Furthermore, we demonstrate the effectiveness of FAMM through analyzing mutual information, compression procedure, and facial landmarks for compressed Deepfake videos. Theoretical analyses illustrate that compression does not affect facial muscle motion feature construction and the differences in designed features exist between the real and Deepfake videos. Extensive experimental results conclude that the proposed method outperforms the state-of-the-art methods in detecting compressed Deepfake videos. More importantly, FAMM achieves comparable detection performance on compressed videos that are over real-world social networks.

In recent years, Light Field (LF) video has grabbed much attention as an emerging form of immersive media. LF collects, through a lens matrix, light information emanating in every direction, and obtains rich information about the scene, providing users with an immersive 6 Degrees of Freedom (DoF) experience. The visual content between different viewpoints is highly homogenized, suggesting the possibility of good compression and encoding. However, most fixed-structure LF coding schemes are difficult to adapt to the real-time requirements of different LF applications and best-effort network conditions causing packet loss. In this paper, we propose a dynamic adaptive LF video transmission scheme that can achieve high compression and yet provide near-distortion-free LF video when the network condition is stable. Additionally, for unstable network conditions a description scheduling algorithm is proposed, which can decode the LF video with the highest possible quality even if partial data cannot be received completely and/or timely. We achieve this by designing a Multiple Description Coding (MDC) based solution to transport the LF video compressed by a Graph Neural Network (GNN) model. Experimental results show that the scheduling algorithm can improve the quality of the decoding results by 3% to 15%. Compared with other similar schemes, our system greatly improves the reliability of the video streaming system against packet loss/error and supports heterogeneous receivers.

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.

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.

Servers have different energy efficiency and energy proportionality (EP) due to their hardware configuration (i.e., CPU generation and memory installation) and workload. However, current virtual machine (VM) scheduling in virtualized environments will saturate servers without considering their energy efficiency and EP differences. This article will discuss EASE, the energy efficiency and proportionality aware VM scheduling approach. EASE first executes customized computing intensive, memory intensive, and hybrid benchmarks to calculate a server's energy efficiency and EP. Then it schedules VMs to servers to keep them working at their peak energy efficiency point (or optimal working range). This step improves the overall energy efficiency of the cluster and the data center. For performance guarantee, EASE migrates VMs from servers under highly contending conditions. The experimental results on real clusters show that power consumption can be saved 37.07% ~ 49.98% in the homogeneous cluster. The average completion time of the computing intensive VMs increases only 0.31 % ~ 8.49%. In the heterogeneous nodes, the power consumption of the computing intensive VMs can be reduced by 44.22 %. The job completion time can be saved by 53.80%.

Mobile edge computing (MEC) is an emerging technology to improve the quality of computation experience for mobile devices. As a promising paradigm to deal with latency-sensitive and computation-intensive tasks, it provides cloud computing capabilities in close proximity to mobile devices in the fifth-generation (5G) networks. As the radio and computational resources are both limited in 5G networks, reducing system latency by task scheduling and resource allocation has gained renewed interests. To minimize the weighted-sum latency of all users in multi-user MEC system, we formulate an optimization problem based on partial offloading strategy. Since the optimization problem is NP-hard, we transform it into a piece-wise convex problem and get the latency-optimal offloading strategy using the sub- gradient method. We further put forward a simplified algorithm which can achieve close-to- optimal performance in linear time. Our proposed strategies are verified by numerical results, which indicate that our algorithms significantly reduce the weighted-sum latency compared with other baseline strategies.

The authors propose to use formatting templates and implicit formatting semantics information for automatic metadata identification and segmentation. The pure texts and their corresponding formatting information including line height, font type, and font size, are recognized in parallel to guide metadata identification. The authors use implicit formatting semantics, such as the change of formatting, formatting templates and implications, explicit formatting layouts, as well as predefined frequently occurred keywords database to increase the extraction accuracy. Unlike other OCR-based approaches, the authors use open source PDFBox package as the basic preprocessing tool to get pure texts and formatting values of the document contents. On top of PDFBox they built their own pipeline program, namely, PAXAT, to implement their approaches for metadata extraction. 10177 papers from arXiv, ACM, ACL and other publicly accessed and institution-subscribed sources are tested. The overall extraction accuracy of title, authors, affiliations, author-affiliation matching are 0.9798, 0.9425, 0.9298, and 0.9109, respectively.

The huge energy consumption in data centers produces not only high electricity bill but also tremendous carbon footprints. Although today's servers and data centers of leading internet companies are more energy efficient than ever before, the fluctuations in external workload and internal resource utilization calls for energy proportional computing. Insight into server energy proportionality can help improve workload placement while also reducing energy consumption. In this paper, we investigate all 477 valid published results of SPECpower_ssj benchmark from 2007 to 2016Q3 and reorganize them by hardware availability year for more accurate analysis on production servers. Through comprehensive analysis we find that: (1) The specious stagnation of energy proportionality in recent years is mainly caused by the adoption of processors of specific microarchitecture and is not the indicative trend of energy proportionality improvement. (2) Microarchitecture evolution has more influence on energy efficiency improvement than energy proportionality. (3) Today's servers' peak energy efficiencies are shifting from 100% resource utilization to 80% or 70% utilization and server energy proportionality improves with such shifting. We then conduct extensive experiments on 4 rack servers to investigate the energy efficiency variations under different hardware configurations, including memory per core installation and processor frequency scaling. Our experiments show that hardware configuration has significant impact on server's energy efficiency. Our findings presented in this paper provide useful insights and guidance to system designers, as well as data center operators for energy proportionality aware workload placement and energy savings.

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.

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.

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.

This paper presents a contention-based Medium Access Control (MAC) protocol, Synchronized Sensor Medium Access Control (SS-MAC), for wireless sensor networks (WSNs). To solve the high energy consumption problem in boundary nodes between virtual clusters in Sensor-MAC (S-MAC), SS-MAC introduces the global synchronization of schedules in WSNs. The novelty of our protocol is that it can improve the connectivity of WSNs compared with S-MAC. We illustrate the efficiency and scalability of the scheme via simulation