We propose a JPEG XS compatible Encryption-then-Compression (EtC) scheme whose scrambling primitive is precinct-wise permutations defined in the discrete wavelet transform (DWT) domain. In this work, we permute LL precincts in the DWT domain using line-order permutations within the precinct height and cross-precinct reordering. This choice yields strong perceptual obfuscation at low computational cost while keeping the encoder unmodified. By designing the scrambling in a precinct-wise manner aligned with the JPEG XS packetization, the impact on rate-distortion (RD) performance is minimized, preserving compression efficiency. On 8K content under Main444.12, the method provides visually effective obfuscation with negligible bit-rate overhead for within-precinct line permutations and small overhead for cross-precinct reordering.

Mobile Edge Computing (MEC) has been successful in proving solid support for delay-sensitive and computation-intensive applications, while invoking channel forecast enlightens a new dimension to further improve the performance. However, separately considering channel forecast and resource management fails in fully exploiting the merit of channel forecast. In this paper, we proceed in two steps. 1) By incorporating channel forecast, we extend the conventional Lyapunov optimization into multi-step-ahead Lyapunov optimization to minimize the queueing delay for non-causal scenario. 2) Based on the obtained insights, we tailor the conventional Long Short Term Memory (LSTM) into Differentiated Randomly Connected LSTM (DR-CLSTM) to obtain a desired trade-off between model complexity and forecast accuracy for the sake of delay minimization. Our simulation results highlight the performance gain of the proposed framework in terms of the system delay.

Millimeter-wave (mmWave) vehicular communication brings new technical challenges on wireless resource management due to the sensitivity to blockages and the directionality property, as conventional beam alignment techniques suffer from large communication overhead. To enable fast base station (BS) association and beam alignment, we propose a lightweight online learning framework by embracing sparse representation (SR) and Gaussian process (GP). To obtain preliminary information of the transmission environment, fingerprint-based method is advocated for static scenarios, while its performance degrades in dynamic scenarios. To incorporate the influence of vehicle motion, we innovatively propose the idea of trajectory-aware fingerprint, which further triggers the following two designs: 1) Trajectory Prediction: We utilize GP to predict the trajectory of moving vehicles. Noticing the utility of the forecast information drops fast with the computational complexity, we propose a differentiated prediction framework to balance accuracy and model complexity to maximize such utility and 2) Fingerprint Adaptation: As the existence of infinite number of trajectories, we approximate a trajectory using grayscale image, and prove the influence of such approximation on throughput is limited. Then, given a predicted trajectory, SR is invoked to perform robust fingerprint adaptation that facilitating resource management. Finally, the simulation results demonstrate the superiority of the proposed framework.

Caching sheds a light on reducing long-distance data transmissions over networks, while raising significant privacy concerns. Moving one step ahead, collaborative edge caching is proposed to facilitate preserving user privacy via reducing the external data exposure. However, it still fails to avert the risk of privacy leakage from nearby edge devices. To tackle this issue, we develop an analytical framework for privacy preserving joint communication and content allocation algorithm for distributed collaborative edge caching systems, in which edge devices collaboratively cache and share the content items based on the dummy-based privacy preservation mechanism. Specifically, we define the system request uncertainty criterion from the perspective of information entropy to measure the privacy preservation performance. Consequently, the closed-form relationship between the system request uncertainty and the resource allocation decisions on both communication resources and content items can be derived. Then, we decompose the NP-hard resource management problem into two parts, and propose 1) an optimal dummy request allocation strategy through investigating special properties of the maximal allocation reward gain and 2) an asymptotically optimal content item allocation strategy with low complexity based on the extract penalty method (EPM), which are iterated to obtain a viable solution, followed by the proof of convergence and asymptotic monotone property. Finally, the performance improvements are verified by simulations.

Content popularity forecast is a key enabler toward the realization of proactive content caching, contributing to significant reduction of content fetching delay. Different from most of the existing literature that concentrating on enhancing the forecast accuracy, we tailor the popularity forecast and user clustering algorithms for improving the caching performance. Specifically, through analyzing the caching performance drop incurred by inaccurate popularity forecast from the Bayesian perspective, we obtain two critical insights, which trigger the following designs: 1) as the utility of forecast varies according to the content rank, we propose a content-caching-oriented popularity forecast algorithm based on Gaussian process (GP), where more computational resource is allocated to forecast the popularity of prioritized contents and 2) to alleviate the influence of forecast error on the rank of prioritized contents, we propose a content-caching-oriented user clustering algorithm based on the K-means algorithm. Since the involved optimization problem is NP-hard, we propose an iterative algorithm, whose convergence property in terms of region stability is proved, as the objective function may vary before a local minima is reached. Finally, the simulation results demonstrate the superiority of the proposed framework.

Network traffic monitoring plays a crucial role in maintaining the security and reliability of the communication networks. Although Machine Learning (ML) assisted abnormal traffic detection has been emerged as a promising paradigm, the existing data-driven learning-based approaches are faced with challenges on inefficient traffic feature extraction and high computational complexity, especially when taking the evolving property of traffic process into consideration. To this end, we establish an online learning framework for abnormality traffic detection by embracing Gaussian Process (GP) and Sparse Representation (SR). The contributions of this paper are two-fold: 1). We utilize a special kernel, i.e., mixture of Gaussian, to better explore and exploit the evolving traffic characteristics, so as to more accurately model network traffic. 2). To combat noise and modeling error, we formulate a feature vector based on Kullback-Leibler (KL) divergence to measure the difference between normal and abnormal traffic, based on which SR is adopted to perform robust binary classification. Finally, we demonstrate the superiority of the proposed framework in terms of detection accuracy through simulation.

To alleviate the local computation demands from the ever-increasing computation-intensive mobile applications, Mobile Edge Computing (MEC) has proved promising. Especially, by opportunistically offloading these computation tasks to the MEC server, the delay of computing could be significantly improved through communication. In this paper, we develop an analytical framework for joint communication and computation resources allocation for multi-user MEC systems. Specifically, to retrieve the combined effect of communication and computation capabilities, we establish a dual queue system, including a data queue sub-system and a computation queue sub-system. To address the associated stochastic resource optimization problem, we propose a low-complexity resource allocation algorithm by Lyapunov optimization to stabilize all the sub-queue systems. As the practical buffers are finite, the conventional delay analysis of Lyapunov optimization becomes inaccurate. Alternatively, we model the stochastic queue lengthes as discrete time controlled random walk processes, which are transformed to continuous time Stochastic Differential Equations (SDEs) with reflections by strong approximation. According to the steady state analysis on the SDEs, we derive closed-form steady state distributions of the queue lengths, and then obtain the average delay performance with finite buffers. Finally, the accuracy of the proposed delay analysis is verified through simulation.

The forecast of network traffic with arbitrary predicting horizon is a key enabler of smart management in next-generation networks, as sufficient amount of time can be provided for the proactive manipulation of network resources to maintain high quality transmission. Nevertheless, the evolving characteristic of network traffic challenges the current learning-based and data-driven algorithms on both prediction accuracy and computational complexity. In this work, we explore special properties of network traffic, which are further encoded into the Gaussian Process (GP)-based online learning framework, so as to better comprehend and predict future network traffic from a Bayesian perspective. Specifically, we proceed by three steps, 1). Observing network traffic is evolving, to explore and exploit the dynamic traffic patterns at different times and time-scales, we try to approximate the optimal kernel function of GP by utilizing a mixture of Gaussian to encode the dominant and several nondominant patterns. 2). As network traffic at different time-scales share several common patterns, we adopt Process Convolution (PConv) to fully exploit correlations among multiple subsequent time-slots, so as to facilitate network traffic forecast with large predicting horizon. 3). To promote the tracking capability of the proposed GP-PConv framework without significantly increasing the number of hyper-parameters to train, we slightly modify the GP-based prediction through Lyapunov optimization, which brings performance improvements both in terms of accuracy and computational complexity. Finally, we demonstrate the superiority of the proposed algorithm through simulation.

In this paper, we propose access control for privacy-preserving Gaussian process regression (GPR), in which the encrypted data are generated through a random unitary transform (RUT). The proposed secure GPR enables computation in both encrypted input and output domains, and the access to inputs and prediction results can be controlled. We prove that our GPR for encrypted data has the same prediction accuracy as GPR for non-encrypted data. Furthermore, we demonstrate the effectiveness of our method by experimenting with diabetes data from the medical analysis field.

Multi-slot-ahead forecasting on network traffic provides an extra degree of freedom to proactively manipulate the network resources when immediate reconfiguration of networks is expensive or infeasible. In return, it challenges the existing data-driven learning-based approaches on accuracy, especially when considering the evolving property of the traffic process. To this end, we establish an adaptive learning framework for multi-slot-ahead network traffic prediction based on Gaussian Process (GP). GP facilitates learning and comprehending the traffic process from a Bayesian perspective, where the main characteristics can be encoded into the kernel function for performance enhancement. The contributions of this paper are two-fold: 1). To track the evolving traffic characteristics, we approximate the optimal kernel adapting to the current traffic. 2). To predict in a large time horizon without significantly hurt the performance, Linear Model of Co-regionalization (LMC) is utilized to better make use of the correlation among subsequent multiple time-slots. Finally, we demonstrate the high tracking capability as well as the superiority of the proposed framework in terms of prediction accuracy through simulation.

With accurate knowledge of future Channel State Information (CSI), it becomes possible to better comprehend the radio propagating environment and manipulate the wireless resources in a proactive manner, so as to provide solid support to smart and high quality wireless transmission. However, in mobile environment, the evolving correlation patterns in CSI series challenge the existing data-driven algorithms to adaptively learn and predict its behavior. In this article, an adaptive learning algorithm is proposed based on Gaussian Process (GP), to discover and utilize the spatial correlation within a channel and across channels, and produce accurate CSI prediction. Specifically, 1). To track the evolving correlation of a channel, we tailor Spectrum Mixture (SM) kernel to not only approximate the optimal kernel adapting to the current CSI, but also capture the combined effect of path loss and User Equipment (UE) motion. 2). The correlation across channels is encoded into the GP-based learning framework through Linear Model of Co-regionalization (LMC). Finally, we verify the performance improvements through simulation.

Big-data analysis by edge/cloud systems is becoming more important. However, when information may lead to personal identification, such information tends to be encrypted and restricted to its owners to ensure privacy protection. The resulting data is often insufficiently detailed to permit useful analysis. As a result, the desired analysis accuracy may not be achieved. To deal with this issue, several studies have examined encryptions based on the random unitary transform. This is because the random unitary transform has lower computational complexity than other encryption schemes, and its encryption domain supports several signal processing algorithms. However, analysis models on distributed encrypted data, have not been studied deeply enough. In this paper, we construct an analysis model for data encrypted with the random unitary transform by deriving a LASSO solution for encrypted data. The analytical model can derive the same LASSO solution as that yielded by processing the original data (i.e. without encryption). The analytical model supports distributed encryption, where a data set consists of different components that are encrypted at different sites independently. The collaboration enables us to improve the accuracy of analysis for distributed privacy-sensitive information.

In this paper, we propose a privacy-preserving pattern recognition method that uses encrypted sparse representations in L0 norm minimization. We prove, theoretically, that the proposal has exactly the same dictionary and sparse coefficient estimation performance as the Label Consistent K-Singular Value Decomposition (LC-KSVD) algorithm for non-encrypted signals. It can be directly implemented by the LC-KSVD algorithm without any modification. Finally, we demonstrate its excellent recognition performance and security strength for the face recognition task using the Extended YaleB database.

Offloading the computationally intensive workloads to the edge and cloud not only improves the quality of computation, but also creates an extra degree of diversity by collecting information from devices in service, which, in turn, has raised significant concerns on privacy as the aggregated information could be misused without the permission by the third party. Sparse coding, which has been successful in computer vision, is finding application in this new domain. In this paper, we develop a secure face recognition framework to orchestrate sparse coding in edge and cloud networks. Specifically, 1). To protect the privacy, we develop a low-complexity encrypting algorithm based on random unitary transform, where its influence on dictionary learning and sparse representation is analysed. We further prove that such influence will not affect the accuracy of face recognition. 2). To fully utilize the multi-device diversity, we extract deeper features in an intermediate space, expanded according to the dictionaries from each device, and perform classification in this new feature space to combat the noise and modeling error.

With accurate network traffic prediction, future communication networks can realize self-management and enjoy intelligent and efficient automation. Benefiting from discovering the sparse property of network traffic in temporal domain, it becomes possible to develop compact algorithms with high accuracy and low computational complexity. For this purpose, we establish an analytical framework for network traffic prediction by extending traditional sparse representation to predictive sparse representation, and try to take the full advantage of such sparsity. Specifically, 1). To equip sparse representation with predictive capability, we divide the historical traffic records into two sets, and jointly train the representative/predictive dictionaries, such that the query point is embedded in terms of a sparse combination of dictionary atoms, and jointly coded with its T+1 time slot behind counterpart. 2). To estimate the sparse code of the query point, we only have to decompose its counterpart into a sparse combination of the representative dictionary atoms by adopting iterative projection method, which provides extra flexibility and adaptability in determining the dependence range. After this, the prediction is performed based on the predictive dictionary. 3). To promote the capability of capturing the rapidly changing traffic, we slightly modify the sparse representation-based prediction by adopting Lyapunov optimization, and minimize the time averaged prediction error. Finally, our proposed algorithm is evaluated by simulation to show its superiority over the conventional schemes.

Exploiting the vacant spectrum resource at mmWave bands provides the potential for fulfilling the requirements of broadband services. Nevertheless, the sensitivity of mmWave to blockages together with its directionality bring new technical challenges in the vehicular context. In particular, traditional beam training is inadequate in satisfying low communication overhead and small latency, and the influence of the same blockage on a fixed position varies according to vehicle motion. To facilitate fast beam alignment, fingerprint-based method stands out as an efficient solution, where the utilities of selecting different beam pairs are recorded in the fingerprint database for reference at a given position. In order to better orchestrate fingerprint-based method with vehicular scenarios, we propose the idea of trajectory-aware fingerprint, which extracts the combined effect of mobility, propagation environment, and blockages, so as to faithfully reflect the transmitting condition in mobile scenarios. Then, a light-weight machine learning framework is established for intelligent adaptation among multiple fingerprints to find a near-optimal BS association and beam alignment solution. Finally, the simulation result verifies the performance improvements.

High frame rate (HFR) video is attracting strong interest since it is considered as a next step toward providing Ultra-High Definition video service. For instance, the Association of Radio Industries and Businesses (ARIB) standard, the latest broadcasting standard in Japan, defines a 120 fps broadcasting format. The standard stipulates temporally scalable coding and hierarchical transmission by MPEG Media Transport (MMT), in which the base layer and the enhancement layer are transmitted over different paths for flexible distribution. We have developed the first ever MMT transmitter/receiver module for 4K/120fps temporally scalable video. The module is equipped with a newly proposed encapsulation method of temporally scalable bitstreams with correct boundaries. It is also designed to be tolerant to severe network constraints, including packet loss, arrival timing offset, and delay jitter. We conducted a hierarchical transmission experiment for 4K/120fps temporally scalable video. The experiment demonstrated that the MMT module was successfully fabricated and capable of dealing with severe network constraints. Consequently, the module has excellent potential as a means to support HFR video distribution in various network situations.

To ensure the reliability of distributed storage systems, we studied network bandwidth-aware multiple fault tolerant erasure codes. A flat XOR code is one of the sparse graph codes designed for distributed storage systems. It encodes and decodes data by exclusive OR (XOR) operation with low complexity. However, conventional construction methods of flat XOR codes are not optimal in terms of repair bandwidth. In this paper, we propose two novel constructions of irregular flat XOR code that can remarkably reduce repair bandwidth compared to conventional flat XOR codes and Reed-Solomon codes. Our irregular flat XOR codes also reduce repair bandwidth compared to flat XOR codes designed by the Monte-Carlo method.

Wireless Data Center Network (Wi-DCN) is considered one of the most promising future data center architectures due to its low installation and management cost and high flexibility of network design. However, the existing Wi-DCN is, still, not capable of providing an efficient big data mining service such as MapReduce because its topology (i.e., Cayley graph with same degree) cannot achieve enough connectivity on the breakdown of servers and spectrum efficiency, which are important factors to improve the performance of big data mining. Therefore, in order to modify the existing Wi-DCN for big data mining, this paper proposes a spherical rack architecture based on a bimodal degree distribution that improves both failure tolerance and spectrum efficiency. Extensive computer simulations demonstrate the effectiveness of our proposed rack architecture in terms of data transmission time required for MapReduce under a failure-prone environment.

Hierarchical unstructured peer-to-peer (P2P) networks for file sharing systems such as Gnutella and Kazaa have made a tremendous achievement in the last decade. However, while these P2P networks can be tolerant to churn, i.e., the dynamics of peer participation and departure (or fault), there still remains the issue of vulnerability to Denial of Service (DoS) attacks, i.e., when the highest degree peers are removed. In order to overcome this shortcoming, we focus on a bimodal degree distribution, which is tolerant to both churn and DoS attacks. However, the network topology affects the network stability that was not taken into considered in the previous works. Therefore, we analyze the optimal network topology for DoS attack tolerance, and accordingly develop the peer joining procedure to construct and maintain the proposed network topology. Our proposed scheme is dubbed THUP (churn/DoS Tolerant, Hierarchical, Unstructured, P2P network). Performance evaluation conducted through computer simulations shows that THUP substantially improves the stability and communication efficiency compared with other existing P2P networking structures.

Digital cinema is a promising application that utilizes high-speed optical networks to transfer super-high-definition (SHD) images. The networks are primarily used for distributing digital cinema contents in packet data form, and are also used to support new services such as the live streaming of musicals and sport games to movie theaters. While current transfer services offer high-definition (HD) quality video, live-streaming applications will soon shift to providing cinema quality 4K content to both business and movie theaters users. The extra-high-quality 4K format enables a realistic telepresence, and will be combined with special tools such as video editing systems to realize effective remote collaboration for business workspaces. This paper introduces successive research on SHD image transmission and its application, especially in digital cinema and associated application fields.

In this paper we propose a method of constructing packet-level LDGM codes that offer backward compatibility with conventional viewing devices. Our proposed method makes it possible to watch content even if the viewer does not support any FEC module. Moreover, the method also improves the coding efficiency through the combined use of packet division and interleaving methods. In general, there is a tradeoff between computation complexity and performance, but our proposed method improves coding efficiency by using a message passing decoding scheme that does not require any additional computation. The coding efficiency of the proposed method is evaluated both experimentally and theoretically.

A new bit allocation scheme for JPEG 2000 systems in parallel-distributed environments is proposed. In recent years, super high-definition images are being used for not only digital cinema also alternative services e.g. real-time sports in theaters. In general, such high-definition images are divided into small segments, such as tiles or components, and compressed. However, this process leads to low coding efficiency. In contrast, our proposed scheme controls the bit allocation ratio for each color-component separately by using the quantizer properties available in the JPEG 2000 standard. Simulation results show that the proposed scheme achieves the same coding performance as the non parallel-distributed approach.

Distributed video coding (DVC) is attracting attention as a paradigm for video compression. One reason is that it transfers the high complexity computation to the decoder. However, the DVC decoder that uses channel coding based on a belief propagation algorithm has complexity exceeding that of the H.264/AVC encoder. This paper proposes a parallelized DVC scheme that treats each bitplane independently. Unfortunately, simple parallelization schemes suffer low compression efficiency because they can't use additional side information for the decoding of subsequent bitplanes. Therefore, we propose an effective estimation method that can calculate the bit probability as accurately as possible by index assignment. Simulations show that the proposed system can reduce the decode time by up to about 30-35 [%] with only slight parallelization loss.

Flickering is a temporal visual artifact that affects compressed video. It is prominent in intra-frame video coders and is largely the result of content variations and quantization. We concentrate on flickering due to quantization. JPEG2000 uses post-compression quantization which is applied through the EBCOT algorithm. EBCOT has been found, however, to cause significant flickering in the reconstructed video. In this work, we evaluate existing flicker metrics, investigate the causes of flicker, and propose a new rate-distortion optimal algorithm that suppresses flicker. The proposed algorithm suppresses temporal flicker at a negligible cost in spatial image quality.

Video sequences encoded with JPEG2000 exhibit flicker artifact. This artifact is not perceivable in still images. In contrast, it is perceived in the temporal domain and is the result of two factors: (a) the image content, and (b) the EBCOT post-compression rate allocation. In this work, we address the second factor. First, we propose an intra-frame rate-allocation scheme that ensures uniform error energy distribution and improves perceptual quality. We then extend this scheme to the temporal domain and further modify it to address flicker. Experimental results show that our algorithm suppresses the flicker artifact without sacrificing good PSNR performance

Distributed video coding (DVC), based on the theorems proposed by Slepian-Wolf and Wyner-Ziv, is attracting attention as a new paradigm for video compression. In DVC systems, several encoders will send bit streams to a single decoder which must handle all incoming bit streams. Some of the DVC systems use intraframe compression based on DCT. However, conventional DVC systems have low affinity with DCT, because they fail to generate the necessary side information until after decompressing all bit streams. In this paper, we propose a new DVC scheme that is an easy way to generate the side information before decompressing all bit streams. The scheme utilizes scalability of JPEG 2000 and the multicomponent transforms of JPEG 2000 part-2. Tests confirm that the PSNR of the new scheme is about 7[dB] higher than that of conventional JPEG 2000

Distributed video coding (DVC), based on the theorems proposed by Slepian-Wolf and Wyner-Ziv, is attracting attention as a new paradigm for video compression. Some of the DVC systems use intra-frame compression based on DCT. Correspondingly, we proposed a wavelet-based DVC system that utilizes the current JPEG 2000 standard. The scheme has scalability with regard to resolution and quality. However, related works does not offer that optimal bit allocation method for each sub-band coefficients. In this report, we propose a bit allocation method for a wavelet-based DVC scheme. Tests confirm that the PSNR is increased about 1-2[dB] by propose method

JPEG2000, an international standard for still image compression, has three main features: 1) high coding performance; 2) unified lossless/lossy compression; and 3) resolution and SNR scalability. Resolution scalability is especially promising given the popularity of super high definition (SHD) images like digital-cinema. Unfortunately, the resolution scalability of its current implementation is restricted to powers of two. In this paper, we introduce non-octave scalable coding with a motion compensated interframe wavelet transform. By using the proposed algorithm, images of rational scales can be decoded from a compressed code stream. Experiments on SHD digital cinema test sequences show the effectiveness of the proposed algorithm.

The features of JPEG2000, an international standard for still image compression, include (1) high coding performance, (2) unified lossless/lossy compression, (3) resolution and SNR scalability. Resolution scalability is an especially promising feature given the popularity of super high definition (SHD) images like digital-cinema. Unfortunately, its current implementation of resolution scalability is restricted to powers of two. We introduce a non-octave resolution conversion method that is compatible with JPEG2000 part2. By using the proposed algorithm, images of various resolutions can be decoded from a compressed JPEG2000 part2 code stream. Experimental results from digital-cinema test sequences show the effectiveness of the proposed algorithm.

This paper proposes a unified coding algorithm for lossless and near-lossless compression of still color images. The algorithm can control the Peak Signal-to-Noise Ratio (PSNR) of the reconstructed image. Furthermore, the distortion on the RGB plane is suppressed to within the level of /spl plusmn/p, where p is a certain small non-negative integer. In order to control the PSNR of the reconstructed image, an adaptive quantizer is designed on the basis of human visual criteria. Experimental results confirm the effectiveness of the proposed algorithm.

This paper proposes a unified coding algorithm for lossless and near-lossless color image compression that exploits the correlations between RGB signals. For lossless coding, a reversible color transform is proposed that removes the correlations between RGB signals while avoiding any finite word length limitation. Next, the lossless algorithm is extended to a unified coding algorithm of lossless and near-lossless compression that can control the distortion level in the magnitude on the RGB plane. Experimental results show the effectiveness of the proposed algorithm.