The concept of wireless virtualized networks enables Mobile Virtual Network Operators (MVNOs) to utilize resources made available by multiple Infrastructure Providers (InPs) to set up a service. Nevertheless, existing centralized resource provisioning approaches fail to address such a scenario due to conflicting objectives among InPs and their reluctance to share private information. This paper addresses the problem of resource provisioning from several InPs for services with geographic coverage constraints. When complete information is available, an Integer Linear Program (ILP) formulation is provided, along with a greedy solution. An alternative coalition formation approach is then proposed to build coalitions of InPs that satisfy the constraints imposed by an MVNO, while requiring only limited information sharing. The proposed solution adopts a hedonic game-theoretic approach to coalition formation. For each InP, the decision to join or leave a coalition is made in a decentralized manner, relying on the satisfaction of service requirements and on individual profit. Simulation results demonstrate the applicability and performance of the proposed solution.

Network slicing is a critical feature in 5G and beyond communication systems, enabling the creation of multiple virtual networks(i.e., slices)on a shared physical network infrastructure. This involves efficiently mapping each slice component, including virtual network functions (VNFs) and their interconnections (virtual links), onto the physical network. This paper considers the slice embedding problem in which the order of VNFs can be adjusted. This provides increased flexibility for service deployment, but the selection of the best order of VNFs also complicates embedding. We propose an optimization framework to tackle the challenges of jointly optimizing slice admission control and embedding with flexible VNF ordering. Additionally, we introduce a near-optimal branch-and-bound (BnB) algorithm, combined with the A* search algorithm, to generate embedding solutions efficiently. Extensive simulations on both small and large-scale multi-tiered 5G networks demonstrate that flexible VNF ordering increases the number of deployable slices within a network infrastructure, thereby improving resource utilization and better meeting diverse demands across varied network topologies.

Remote inference allows lightweight edge devices, such as autonomous drones, to perform vision tasks exceeding their computational, energy, or processing delay budget. In such applications, reliable transmission of information is challenging due to high variations of channel quality. Traditional approaches involving spatio-temporal transforms, quantization, and entropy coding followed by digital transmission may be affected by a sudden decrease in quality (thedigital cliff) when the channel quality is less than expected during design. This problem can be addressed by using Linear Coding and Transmission (LCT), a joint source and channel coding scheme relying on linear operators only, allowing to achieve reconstructed per-pixel error commensurate with the wireless channel quality. In this paper, we propose CV-Cast: the first LCT scheme optimized for computer vision task accuracy instead of per-pixel distortion. Using this approach, for instance at 10 dB channel signal-to-noise ratio, CV-Cast requires transmitting 28% less symbols than a baseline LCT scheme in semantic segmentation and 15% in object detection tasks. Simulations involving a realistic 5G channel model confirm the smooth decrease in accuracy achieved with CV-Cast, while images encoded by JPEG or learned image coding (LIC) and transmitted using classical schemes at low Eb/N0 are subject to digital cliff.

In the above article [1], on page 1151, eq. (6), there is an error in the equation. The correct equation is: \begin{equation*} \min.\,\,D,\,\,\text{s.t.} \sum\limits_{k = 1}^K {{{\lambda }_k}\beta _k^2 \leqslant P.} \tag{6} \end{equation*} min.D,s.t.∑k=1Kλkβk2⩽P.(6)

Network slicing has appeared a key feature in 5G and beyond communication networks that enables the creation of multiple virtual networks (i.e., slices) over a shared physical network infrastructure. This process involves efficiently embedding (or mapping) each slice element, including virtual network functions (VNFs) and their interconnections, onto the physical network. This paper explores a scenario where the order of VNFs can be adjusted during slice embedding, offering greater flexibility to increase the number of services deployed on the infrastructure. We formulate a novel optimization framework to tackle the challenges of slice admission control and embedding with this flexibility. A heuristic is also introduced to derive embedding solutions in a timely manner. Simulation results demonstrate that allowing flexible VNF ordering significantly increases the number of slices that can be deployed in the network infrastructure.

This paper presents a two-stage Multiple-Model Compression (MMC) approach for sampled electrical waveforms. To limit latency, the processing is window-based, with a window length commensurate to the electrical period. For each window, the first stage compares several parametric models to get a coarse representation of the samples. The second stage then compares different residual compression techniques to minimize the norm of the reconstruction error. The allocation of the rate budget among the two stages is optimized. The proposed MMC approach provides better signal-to-noise ratios than state-of-the-art solutions on periodic and transient waveforms.

Image communication increasingly involves machine-to-machine delivery. For example, images acquired by an autonomous drone can be compressed and sent to an edge server over a wireless network for resource-intensive processing. Traditional compression techniques involving transform, quantization, and entropy coding reach high compression efficiency, but channel conditions worse than expected may lead to a sharp decrease in the decoded image quality. As an alternative, Linear Coding and Transmission (LCT) systems have been proposed to avoid this digital cliff problem: The reconstructed image quality decreases gradually as channel conditions degrade. This paper presents a comprehensive evaluation of computer vision tasks with input images processed and transmitted using LCT. It also analyses the benefits of network retraining, accounting for impairments due to LCT and noisy channel. Considering object detection and semantic segmentation over images transmitted and received by LCT systems, we show that the task accuracy degrades smoothly when the channel quality decreases, avoiding the cliff effect. Retraining with noisy images processed by LCT restores detection mAP degradation from 23.8% to 4.4% and segmentation mIoU degradation from 43.2% to 8.1 % when the channel signal-to-noise ratio is 10 dB.

Applications such as teleoperated driving, remote robot control, and telepresence rely on video services to ensure real-time interaction with a satisfying quality of experience. Reducing the Glass-to-Glass (G2G) delay, i.e., the time delay between the acquisition of a video frame and its display on a remote terminal is critical for these applications. Deep learning-based video frame extrapolation before video encoding has been recently considered as an interesting solution to reduce G2G delay, however, the latency introduced by extrapolation has not been taken into account. In this paper, considering the main sources of latency, including extrapolation delay, we examine the benefits and limitations of frame extrapolation at encoder in reducing the G2G delay in a point-to-point video transmission system. To this end, we compare the latency-quality trade-off for two latency compensation methods: encoding rate reduction and video frame extrapolation. Our aim is to determine the G2G delay reduction that may be achieved at the price of a given quality reduction. Our experiments show that extrapolation methods can provide a null perceived G2G delay with an acceptable loss in quality, particularly for applications with video contents with limited temporal information. Such delay reduction is unreachable via encoding rate reduction.

This article considers the cooperative search and track of moving targets by unmanned aerial vehicles (UAVs) over some regions of interest (RoIs). The RoI contains obstacles that may partly hide the targets. UAVs are not aware of the obstacles' locations and cannot determine when an obstacle limits their field of view. In this article, no map of the RoI is built. This reduces the computational complexity but makes the selection of an appropriate point of view to observe a specific part of the RoI difficult. Showing the absence of a target at a given location of the RoI is then challenging. To address this problem, we introduce a detectability set for each point of the RoI as the set of all UAV locations from where that point is visible. The detectability sets are unknown to the UAVs and evolve with time when the environment is time varying. We assume that each detectability set contains at least one half-cone with a minimal aperture, which translates the fact that targets are never fully occluded by the environment. We prove that a finite number of$L$simultaneous observations are sufficient to guarantee the presence or absence of targets at a given location. This leads to a partition of the fleet into groups of$L$UAVs that simultaneously collect measurements on a specific zone of the RoI. Assuming that state perturbations and measurement noises are bounded, a distributed set-membership estimator is used to evaluate set estimates for potential target locations. The trajectories of the UAVs are designed using a model-predictive control approach to reduce the estimation uncertainty. Simulations illustrate the performance of the proposed approach in the presence of unknown static or moving obstacles.

In the past few years, several efforts have been devoted to reduce individual sources of latency in video delivery, including acquisition, coding and network transmission. The goal is to improve the quality of experience in applications requiring real-time interaction. Nevertheless, these efforts are fundamentally constrained by technological and physical limits. In this paper, we investigate a radically different approach that can arbitrarily reduce the overall latency by means of video extrapolation. We propose two latency compensation schemes where video extrapolation is performed either at the encoder or at the decoder side. Since a loss of fidelity is the price to pay for compensating latency arbitrarily, we study the latency-fidelity compromise using three recent video prediction schemes. Our preliminary results show that by accepting a quality loss, we can compensate a typical latency of 100 ms with a loss of 8 dB in PSNR with the best extrapolator. This approach is promising but also suggests that further work should be done in video prediction to pursue zero-latency video transmission.

In this paper, the situation in which a receiver has to execute a task from a quantized version of the information source of interest is considered. The task is modeled by the minimization problem of a general cost function f(x;g) for which the decision x has to be taken from quantized parameters g. Especially, we focus on the particular scenario where the decision space is a convex polyhedron with cost function being convex. Furthermore, we propose a new goal-oriented quantization algorithm by combining the procedure of iteratively expanding and reinstating decision set together with Jensen’s inequality. Proposed method could also be extended to some non-convex scenarios, namely, weakly convex cost function whose eigenvalues of Hessian matrix w.r.t decision x are lower-bounded. Numerical results show that proposed algorithm can considerably reduce the optimality loss (OL) compared to conventional approaches or the required number of quantization bits to achieve a certain relative optimality loss.

Network slicing has emerged as a key concept in 5G systems, allowing Mobile Network Operators (MNOs) to build isolated logical networks (slices) on top of shared infrastructure networks managed by Infrastructure Providers (InP). Network slicing requires the assignment of infrastructure network resources to virtual network components at slice activation time and the adjustment of resources for slices under operation. Performing these operations just-in-time, on a best-effort basis, comes with no guarantee on the availability of enough infrastructure resources to meet slice requirements. This paper proposes a prioritized admission control mechanism for concurrent slices based on an infrastructure resource reservation approach. The reservation accounts for the dynamic nature of slice requests while being robust to uncertainties in slice resource demands. Adopting the perspective of an InP, reservation schemes are proposed that maximize the number of slices for which infrastructure resources can be granted while minimizing the costs charged to the MNOs. This requires the solution of a max-min optimization problem with a non-linear cost function and non-linear constraints induced by the robustness to uncertainties of demands and the limitation of the impact of reservation on background services. The cost and the constraints are linearized and several reduced-complexity strategies are proposed to solve the slice admission control and resource reservation problem. Simulations show that the proportion of admitted slices of different priority levels can be adjusted by a differentiated selection of the delay between the reception and the processing instants of a slice resource request.

Network slicing has emerged as a pivotal concept in 5G systems, allowing mobile operators to build isolated logical networks (slices) on top of shared infrastructure networks. Within a network slice, several Service Function Chains are usually deployed on a best-effort premise. Nevertheless, this approach does not guarantee the availability of enough infrastructure resources to accommodate the uncertain and time-varying slice resource demands.This paper investigates two adaptive slice resource provisioning methods accounting for the evolution with time of the slice resource demands. A probabilistic guarantee of meeting the slice resource requirements can be obtained, while being robust against uncertainties. The myopic approach accounts for the past demands when provisioning the current demands, while the foresighted approach accounts for both past and future demands. These two methods lead to MILP problems. Their performance is compared with a quasi-static method, where provisioning is agnostic of the past and future demands.

In this paper, we propose a new inter-dependent Rate-QP-Distortion model. This model predicts the size of the picture after compression based on the distortion (D) of the reference frame and the current Quantization parameter (QP). This model is particularly useful when adjusting the QP of the picture according to the allocated bitrate budget. Simulation results demonstrate that the proposed model outperforms other models in the literature. In the video sequence Tango, up to 90% of all prediction errors are inferior to 8.6% when using constant QP encoding, and 90% of all prediction errors are inferior to 12% when using variable QP encoding. One application of this model is in low latency video streaming, where each frame of the video sequence needs to be coded with a specific target bitrate, due to variations of the instantaneous transmission rate.

The SoftCast scheme has been proposed as a promising alternative to traditional video broadcasting systems in wireless environments. In its current form, SoftCast performs image decoding at the receiver side by using a Linear Least Square Error (LLSE) estimator. Such approach maximizes the reconstructed quality in terms of Peak Signal-to-Noise Ratio (PSNR). However, we show that the LLSE induces an annoying blur effect at low Channel Signal-to-Noise Ratio (CSNR) quality. To cancel this artifact, we propose to replace the LLSE estimator by the Zero-Forcing (ZF) one. In order to better understand the perceived quality offered by these two estimators, a mathematical characterization as well as an objective and subjective studies are performed. Results show that the gains brought by the LLSE estimator, in terms of PSNR and Structural SIMiliraty (SSIM), are limited and quickly tend to null value as the CSNR increases. However, higher gains are obtained by the ZF estimator when considering the recent Video Multi-method Assessment Fusion (VMAF) metric proposed by Netflix, which evaluates the perceptual video quality. This result is confirmed by the subjective assessment.

Network slicing allows Mobile Network Operators to split the physical infrastructure into isolated virtual networks (slices), managed by Service Providers to accommodate customized services. The Service Function Chains (SFCs) belonging to a slice are usually deployed on a best-effort premise: nothing guarantees that network infrastructure resources will be sufficient to support a varying number of users, each with uncertain requirements. Taking the perspective of a network Infrastructure Provider (InP), this article proposes a resource provisioning approach for slices, robust to a partly unknown number of users with random usage of the slice resources. The provisioning scheme aims to maximize the total earnings of the InP, while providing a probabilistic guarantee that the amount of provisioned network resources will meet the slice requirements. Moreover, the proposed provisioning approach is performed so as to limit its impact on low-priority background services, which may co-exist with slices in the infrastructure network. Taking all these constraints into account leads to an integer programming problem with many nonlinear constraints. These constraints are first relaxed to get an integer linear programming formulation of the slice resource provisioning problem. This problem is then solved considering the slice resource provisioning demands jointly. A suboptimal approach is finally proposed where slice resource provisioning demands are considered sequentially. Both solutions are compared to provisioning schemes that do not account for best-effort services sharing the common infrastructure network, as well as uncertainties in the slice resource demands.

With network slicing, Mobile Network Operators can accommodate on a common network infrastructure various customized services from Service Providers (SPs). Usually, the Service Function Chains belonging to a slice are deployed on a best-effort basis. Nothing ensures that enough infrastructure resources can be allocated to satisfy the demands of SPs. This paper introduces a radio resources provisioning approach to satisfy the demands of slices with radio coverage constraints. By provisioning, we ensure that enough resources are reserved for further SFC deployment. Numerical results show the effectiveness of the proposed provisioning framework for a slice deployment on a mobile network infrastructure satisfying a minimum data rate for users in the geographical areas where services have to be made available.

This paper introduces a new formal mathematical problem initially motivated by an application of Network Coding (NC) to Information Centric Networks (ICN). It is of more limited scope but is remotely inspired by the well-known index coding problem. It is presented as follows: "given a vector space, can one construct several subsets of vectors, such that when drawing arbitrarily one vector from each subset, the selected vectors would be always linearly independent?". Answering this question is a step to construct an ICN efficient scheme with NC. We prove that our previously introduced construction is the only possible solution for a large family of constructions. This is an important result by itself. It also implies that any alternate solutions are outside this family and we propose one example.

SoftCast-based linear video coding and transmission (LVCT) schemes have been proposed as a promising alternative to traditional video coding and transmission schemes in wireless environments. Currently, the performance of LVCT schemes is evaluated by means of traditional objective scores such as PSNR or SSIM. Nevertheless, since the compression is performed in a very different way from traditional coding schemes such as HEVC, visual artifacts are also quite different and deserve to be subjectively assessed. In this paper, we propose a subjective quality assessment of SoftCast, pioneer and standard of the LVCT schemes. This study aims to better understand the trade-offs between the LVCT parameters that can be tuned to improve the quality. These parameters, including different GoP-sizes, Compression Ratios (CR) and Channel Signal-to-Noise Ratio (CSNR), are used to generate a dataset of 85 videos. A Double Stimulus Impairment Scale (DSIS) test is performed on the received videos to assess the perceived quality. Results show that the key characteristic of SoftCast, the linear relation between CSNR and PSNR, is also observed with the Mean-Opinion Scores (MOS), except at high CSNR where the quality saturates. In addition, Bjøntegaard model is used to quantify the trade-offs between CR, GoP-size and CSNR, depending on the intended application. Finally, the performance of objective metrics compared to the obtained MOS is evaluated. Results show that Multi-Scale SSIM (MS-SSIM), SSIM and Video Multimethod Assessment Fusion (VMAF) metrics offer the best correlation with the MOS values.

Data acquired over long periods of time like High Definition (HD) videos or records from a sensor over long time intervals, have to be efficiently compressed, to reduce their size. The compression has also to allow efficient access to random parts of the data upon request from the users. Efficient compression is usually achieved with prediction between data points at successive time instants. However, this creates dependencies between the compressed representations, which is contrary to the idea of random access. Prediction methods rely in particular on reference data points, used to predict other data points. The placement of these references balances compression efficiency and random access. Existing solutions to position the references use ad hoc methods. In this paper, we study this joint problem of compression efficiency and random access. We introduce the storage cost as a measure of the compression efficiency and the transmission cost for the random access ability. We express the reference placement problem that trades storage with transmission cost as an integer linear programming problem. Considering additional assumptions on the sources and coding methods reduces the complexity of the search space of the optimization problem. Moreover, we show that the classical periodic placement of the references is optimal, when the encoding costs of each data point are equal and when requests of successive data points are made. In this particular case, a closed-form expression of the optimal period is derived. Finally, the proposed optimal placement strategy is compared with an ad hoc method, where the references correspond to sources where the prediction does not help reducing significantly the encoding cost. The proposed optimal algorithm shows a bit saving of -20% with respect to the ad hoc method.

This paper considers the problem of impulse noise mitigation when video is encoded using a SoftCast-based Linear Video Coding (LVC) scheme and transmitted using an Orthogonal Frequency-Division Multiplexing (OFDM) scheme for multi-carrier modulation over a wideband channel prone to impulse noise. In the time domain, the impulse noise is modeled as realization of a sequence of independent and identically distributed Bernoulli-Gaussian variables. A Fast Bayesian Matching Pursuit algorithm is employed for impulse noise mitigation. This approach requires the provisioning of some OFDM subchannels to estimate the impulse noise locations and amplitudes. Provisioned subchannels cannot be used to transmit data and lead to a decrease of the video quality at receivers in absence of impulse noise. Using a phenomenological model (PM) of the residual noise variance after impulse mitigation in the subchannels, we have proposed an algorithms that is able to get the amount of subchannel to provision which minimizes the mean-square error of the decoded video at receivers. Simulation results show that the PM can accurately predict the number of subchannels to provision and that impulse noise mitigation can significantly improve the decoded video quality compared to a situation where all subchannels are used for data transmission.

With network slicing in 5G networks, Mobile Network Operators can create various slices for Service Providers (SPs) to accommodate customized services. Usually, the various Service Function Chains (SFCs) belonging to a slice are deployed on a best-effort basis. Nothing ensures that the Infrastructure Provider (InP) will be able to allocate enough resources to cope with the increasing demands of some SP. Moreover, in many situations, slices have to be deployed over some geographical area: coverage as well as minimum per-user rate constraints have then to be taken into account. This paper takes the InP perspective and proposes a slice resource provisioning approach to cope with multiple slice demands in terms of computing, storage, coverage, and rate constraints. The resource requirements of the various SFCs within a slice are aggregated within a graph of Slice Resource Demands (SRD). Infrastructure nodes and links have then to be provisioned so as to satisfy all SRDs. This problem leads to a Mixed Integer Linear Programming formulation. A two-step approach is considered, with several variants, depending on whether the constraints of each slice to be provisioned are taken into account sequentially or jointly. Once provisioning has been performed, any slice deployment strategy may be considered on the reduced-size infrastructure graph on which resources have been provisioned. Simulation results demonstrate the effectiveness of the proposed approach compared to a more classical direct slice embedding approach.

This paper considers the problem of impulse noise mitigation for videos encoded using a SoftCast-based Linear Video Coding (LVC) scheme and transmitted using an OFDM scheme over a wideband channel prone to impulse noise. In the time domain, the impulse noise is modeled as realizations of iid Bemoulli-Gaussian variables. A Fast Bayesian Matching Pursuit algorithm is employed for impulse noise mitigation. This approach requires the provisioning of some OFDM subchannels to estimate the impulse noise locations and amplitudes. Provisioned subchannels cannot be used to transmit data and lead to a decrease of the nominal decoded video quality at receivers in absence of impulse noise. Using a phenomenological model (PM) of the residual noise variance after impulse correction, an algorithm is proposed to evaluate the optimal number of subchannels to provision for impulse noise mitigation. Simulation results show that the PM can accurately predict the number of subchannels to provision and that impulse noise mitigation can significantly improve the decoded video quality compared to a situation where all subchannels are used for data transmission.

The conventional approach to pre-process data for compression is to apply transforms such as the Fourier, the Karhunen-Loeve, or wavelet transforms. One drawback from adopting such an approach is that it is independent of the use of the compressed data, which may induce significant optimality losses when measured in terms of final utility (instead of being measured in terms of distortion). We therefore revisit this paradigm by tayloring the data pre-processing operation to the utility function of the decision-making entity using the compressed (and therefore noisy) data. More specifically, the utility function consists of an Lp-norm, which is very relevant in the area of smart grids. Both a linear and a non-linear use-oriented transforms are designed and compared with conventional data pre-processing techniques, showing that the impact of compression noise can be significantlv reduced.

In Sequential Massive Random Access (SMRA) [1, 2], a set of correlated sources is jointly encoded and stored on a server, and clients want to access to only a subset of the sources. Since the number of simultaneous clients can be huge, the server is only authorized to extract a bitstream from the stored data: no re-encoding can be performed before the transmission of a request. In this paper, we investigate the SMRA performance of lossy source coding of Gaussian sources with memory. In practical applications such as Free Viewpoint Television, this model permits to take into account not only inter but also intra correlation between sources. For this model, we provide the storage and transmission rates that are achievable for SMRA under some distortion constraint, and we consider two particular examples of Gaussian sources with memory.

Network slicing has recently appeared as a key enabler for the future 5G networks where Mobile Network Operators (MNO) create various slices for Service Providers (SP) to accommodate customized services. As network slices are operated on a common network infrastructure owned by some Infrastructure Provider (InP), sharing the resources across a set of network slices is highly important for future deployment. In this paper, taking the InP perspective, we propose an optimization framework for slice resource provisioning addressing multiple slice demands in terms of computing, storage, and wireless capacity. We assume that the aggregated resource requirements of the various Service Function Chains to be deployed within a slice may be represented by a graph of slice resource demands. Infrastructure nodes and links have then to be provisioned so as to satisfy these resource demands. A Mixed Integer Linear Programming formulation is considered to address this problem. A realistic use case of slices deployment over a mobile access network is then considered. Simulation results demonstrate the effectiveness of the proposed framework for network slice provisioning.

This paper considers a mobile multi-agent system (e.g., a crowdsensing or crowdsourcing application) in which agents are characterized by their discrete-valued Level of Ability (LoA) at doing some sensing or data processing task. Agents are not aware of their LoA and are willing to estimate it without the help of a central classification authority, in order to determine whether their contribution will improve or degrade the performance of the global network. Using their estimated LoA, agents may then voluntarily restrain themselves to participate to the activity of the multi-agent system, without being banned by some central control authority. For that purpose, agents, when they meet, perform pairwise comparison tests (PCT) able to determine which is the best agent of a pair of competing agents. Two maximum \emph{a posteriori} (MAP) estimators of the LoA of each agent are proposed using the results of several PCTs. These MAP estimators are then employed to determine, for a given agent, the LoA of the next opponent that minimizes its probability of LoA estimation error. Simulations results show that the proposed optimal opponent selection approach provides better results than simply choosing opponents at random, or always choosing the opponents with the best LoA.

This paper considers the problem of data collection in a sensor network using network coding (NC). It proposes a decoding approach, called HAND, which does not require source packets to be supplemented with NC headers (with encoding vectors), classically used to decode the network-coded packets. HAND exploits the structure imposed by the communication protocol on the packet headers to estimate the original source packets from the received network-coded packets. Network-decoded packets are obtained as the solution of systems of linear equations. The decoding complexity is only one order of magnitude larger than that of classical network decoding.

Linear video coding (LVC) is a promising alternative to classical video coding when video has to be transmitted to wireless receivers experiencing different and time-varying channel conditions. This paper addresses the LVC channel precoding and decoding matrix design when the transmission channel consists of several sub-channels, each with its own power constraint. Such constraints may be found, e.g., in multi-antenna, DSL, or powerline transmission systems. In a previous paper, it has been shown that this matrix design problem may be addressed by an adaptation to LVC of a multi-level water-filling solution proposed for MIMO channels. Here, two suboptimal low-complexity multi-level water-filling techniques are proposed, with different trade-offs between complexity and efficiency. Extensive simulations show that the suboptimal solutions perform very close to the optimal one, with a sensibly reduced complexity.

Consensus among different entities is a fundamental feature of distributed systems, as it is the prerequisite for complex tasks such as distributed coordination of autonomous agents, network synchronization and localization in wireless sensor networks (WSNs). This paper introduces a novel iterative algorithm which is capable of achieving the distributed average consensus in a finite amount of time. This algorithm exploits the possibility of storing information received from neighboring nodes at each iteration. Moreover, we propose an adaptation to the distributed average consensus problem of the Tagged and Aggregated Sums (TAS) algorithm, which we introduced in a previous paper for the distributed confidence region evaluation. The performance of both algorithms is investigated through simulation and compared with state-of-the-art approaches.

Propagation of faulty data is a critical issue. In case of Delay Tolerant Networks (DTN) in particular, the rare meeting events require that nodes are efficient in propagating only correct information. For that purpose, mechanisms to rapidly identify possible faulty nodes should be developed. Distributed faulty node detection has been addressed in the literature in the context of sensor and vehicular networks, but already proposed solutions suffer from long delays in identifying and isolating nodes producing faulty data. This is unsuitable to DTNs where nodes meet only rarely. This paper proposes a fully distributed and easily implementable approach to allow each DTN node to rapidly identify whether its sensors are producing faulty data. The dynamical behavior of the proposed algorithm is approximated by some continuous-time state equations, whose equilibrium is characterized. The presence of misbehaving nodes, trying to perturb the faulty node detection process, is also taken into account. Detection and false alarm rates are estimated by comparing both theoretical and simulation results. Numerical results assess the effectiveness of the proposed solution and can be used to give guidelines for the algorithm design.

Getting confidence regions for parameter estimates obtained from data collected by a wireless sensor network (WSN) is very important to assess the performance of the estimator. The sign perturbed sums (SPS) approach has been proposed recently to defined exact confidence regions in a centralized setting even if only few measurements are available. SPS may be distributed to get confidence regions at each node of a WSN. This paper investigates a data dissemination strategy called Tagged and Aggregated Sums (TAS), exploiting the particularities of SPS, to efficiently provide each node with the information necessary to evaluate locally the confidence region. TAS and flooding (FL) algorithms have been investigated through simulations and then implemented on commercial sensor nodes. The impact of collision avoidance mechanisms at the medium access control (MAC) layer is also experimentally assessed. Performance comparisons show that TAS outperforms FL in structured networks.1

This paper considers a delay tolerant network consisting of nodes equipped with sensors, some of them producing outliers. A distributed faulty node detection (DFD) algorithm, whose aim is to help each node in estimating the status of its sensors, has been proposed recently by the authors. The aim of this paper is to analyze the robustness of the DFD algorithm to the presence of misbehaving nodes performing Byzantine attacks. Two types of attacks are considered and analyzed, each trying to mislead the other nodes in the estimation of the status of their sensors. This provides insights on the way the parameters of the DFD algorithm should be adapted to minimize the impact of misbehaving nodes. Theoretical results are illustrated with simulations considering nodes with random displacements, as well as traces of node inter-contact times from real databases.

This paper addresses the problem of estimating the location of Internet of Things (IoT) Long Range Wide Area Networks (LoRaWAN) devices from time of arrival differences measured at gateways. An Extended Kalman Filter (EKF) based approach is considered to aggregate the measurements obtained at different time instants. Particular attention is paid to the processing of outliers. Based on experimental data obtained from field measurements conducted on a real LoRaWAN™ network an insight into the realistic localization accuracy of the considered localization approach is provided.

This work analyses the impact of channel access issues and packet losses on a distributed defective sensor detection algorithm. A theoretical analysis is performed to characterize the detection performance. Matlab simulation results for the detection algorithm are then provided. Finally, experimental results conducted on a real wireless sensor network involving the IEEE 802.15.4 standard are reported.

Due to their inherent feature of exhibiting frequent disconnections, propagation of faulty data in Delay Tolerant Networks can be a critical aspect to counteract. Indeed the rare meeting events require that nodes are effective and efficient in propagating the correct information. Accordingly mechanisms to rapidly identify possible faulty or misbehaving nodes should be searched. Distributed fault detection has been addressed in the literature in the context of sensor and vehicular networks, but unfortunately these solutions suffer for long delays in identifying and isolating misbehaving nodes. In this paper instead we propose a fully distributed, easily implementable, and fast convergent approach to allow each DTN node to rapidly identify whether its sensors are producing outliers. The behavior of the proposed algorithm is described by some continuous-time state equation, whose equilibrium is characterized. Detection and false alarm rates are estimated by comparing both theoretical and simulation results. Numerical results assess the effectiveness of the proposed solution and can give guidelines in the design of the algorithm.

This paper considers the Softcast joint source-channel video coding scheme for data transmission over parallel channels with different power constraints and noise characteristics, typical in DSL or PLT channels. To minimize the mean square error at receiver, an optimal precoding matrix design problem has to be solved, which requires the solution of an inverse eigenvalue problem. Such solution is taken from the MIMO channel precoder design literature. Alternative suboptimal precoding matrices are also proposed and analyzed, showing the efficiency of the optimal precoding matrix within Softcast, which provides gains increasing with the encoded video quality.

This paper addresses the information theoretical analysis of data compression achieved by random linear network coding in wireless sensor networks. A sparse network coding matrix is considered with columns having possibly different sparsity factors. For stationary and ergodic sources, necessary and sufficient conditions are provided on the number of required measurements to achieve asymptotically vanishing reconstruction error. To ensure the asymptotically optimal compression ratio, the sparsity factor can be arbitrary close to zero in absence of additive noise. In presence of noise, a sufficient condition on the sparsity of the coding matrix is also proposed.

This paper analyzes the impact on the stability of the TCP-Reno congestion control mechanism when a network coding (NC) layer is inserted in the TCP/IP stack. A model of the dynamics of the TCP-NC protocol combined with random early detection (RED) as active queue management mechanism is considered to study the network equilibrium and stability properties. The existence and uniqueness of an equilibrium point is demonstrated and characterized in terms of average throughput, loss rate, and queue length. Global stability is proved in absence of forward delay, and the effects of the NC redundancy factor and of the delay on the local stability of TCP-NC-RED are studied around the equilibrium. The fairness of TCP-NC with respect to TCP-Reno-like protocols is also studied. A version of TCP-NC with adaptive redundancy factor (TCP-NCAR) is also introduced. Results provided by the proposed model are compared to those obtained by simulation for N sources sharing a single link. TCP-NC-RED becomes unstable when delay or capacity increases, as TCP-Reno does, but also when the redundancy factor increases. Its stability region is characterized as a function of the redundancy factor. If TCP-NC and TCP-Reno share the same links, TCP-NC is fair with TCP-Reno-like protocols when no redundancy is added. Simulations show that TCP-NCAR is able to compensate losses on the wireless parts of the network.

The universal proliferation of mobiles devices, and specifically of smartphones with rich sensing capabilities, has given rise to a new fast-growing paradigm of sensing: mobile crowdsensing. Mobile crowdsensing (MCS) takes advantage of the ubiquity of the devices to process and collect information through voluntary sensing.

To guarantee its integrity, a wireless sensor network needs to efficiently detect faulty nodes producing erroneous measurements. This paper proposes a fully distributed fault detection algorithm. A node first collects the measurements of its neighborhood, processes them to decide whether they contain outliers, and broadcasts the result. Then, it decides autonomously about its functioning status. The detection algorithm is proposed in two variants, depending on the proportion of faulty nodes in the network. A theoretical analysis of the probability of error and of the convergence of the algorithm is provided. The tradeoff between false alarm probability and detection probability is characterized using simulation.

This paper introduces Shannon-Kotelnikov (SK) mapping in the SoftCast joint source-channel video coding scheme. On bandwidth constrained channels, the performance of SoftCast saturates, due to the large amount of data (chunks) dropped to match the bandwidth requirements. Using SK mapping, it is possible to increase the number of chunks that may be transmitted without increasing the bandwidth requirements. The resulting scheme has an increased number of design parameters for which we present a transmission-power constrained optimization. This extends range of channel SNRs over which the PSNR gracefully increases and improves the end-to-end performance at medium to high SNRs. The price to be paid is a performance degradation at low SNRs.

In this paper, we investigate the problem of designing good non-binary LDPC codes for Slepian-Wolf coding. The design method is based on Density Evolution which gives the asymptotic error probability of the decoder for given code degree distributions. Density Evolution was originally introduced for channel coding under the assumption that the channel is symmetric. In Slepian-Wolf coding, the correlation channel is not necessarily symmetric and the source distribution has to be taken into account. In this paper, we express the non-binary Density Evolution recursion for Slepian-Wolf coding. From Density Evolution, we then perform code degree distribution optimization using an optimization algorithm called differential evolution. Both asymptotic performance evaluation and finite-length simulations show the gain at considering optimized degree distributions for SW coding.

Computing a tight inner approximation of the range of a function over some set is notoriously difficult, way beyond obtaining outer approximations. We propose here a new method to compute a tight inner approximation of the set of reachable states of non-linear dynamical systems on a bounded time interval. This approach involves affine forms and Kaucher arithmetic, plus a number of extra ingredients from set-based methods. An implementation of the method is discussed, and illustrated on representative numerical schemes, discrete-time and continuous-time dynamical systems.

This paper presents a joint protocol-channel decoding techniques to improve the estimation quality of the type of a packet corrupted by transmission errors. It exploits the redundancy present in the protocol stack to determine the packet type which is the most probable a posteriori. An optimal and a suboptimal estimation algorithm are presented. The latter is illustrated on the type estimation of packets compressed with the Robust Header Compression algorithm in unidirectional mode. Compared to a classical packet type estimation technique, improvements of more than 3.9 dB are observed in terms of channel SNR at header error rates of 10-2.1

This paper proposes a control scheme for the quality-fair delivery of several encoded video streams to mobile users sharing a common wireless resource. Video quality fairness, as well as similar delivery delays are targeted among streams. The proposed controller is implemented within some aggregator located near the bottleneck of the network. The transmission rate among streams is adapted based on the quality of the already encoded and buffered packets in the aggregator. Encoding rate targets are evaluated by the aggregator and fed back to each remote video server (fully centralized solution), or directly evaluated by each server in a distributed way (partially distributed solution). Each encoding rate target is adjusted for each stream independently based on the corresponding buffer level or buffering delay in the aggregator. Communication delays between the servers and the aggregator are taken into account. The transmission and encoding rate control problems are studied with a control-theoretic perspective. The system is described with a multi-input multi-output model. Proportional Integral (PI) controllers are used to adjust the video quality and control the aggregator buffer levels. The system equilibrium and stability properties are studied. This provides guidelines for choosing the parameters of the PI controllers. Experimental results show the convergence of the proposed control system and demonstrate the improvement in video quality fairness compared to a classical transmission rate fair streaming solution and to a utility max-min fair approach.

This paper considers the problem of lossless source coding with side information at the decoder, when the correlation model between the source and the side information is uncertain. Four parametrized models representing the correlation between the source and the side information are introduced. The uncertainty on the correlation appears through the lack of knowledge on the value of the parameters. For each model, we propose a practical coding scheme based on non-binary Low Density Parity Check Codes and able to deal with the parameter uncertainty. At the encoder, the choice of the coding rate results from an information theoretical analysis. Then we propose decoding algorithms that jointly estimate the source vector and the parameters. As the proposed decoder is based on the Expectation-Maximization algorithm, which is very sensitive to initialization, we also propose a method to produce first a coarse estimate of the parameters.

The objective of this paper is the characterization of the Wyner-Ziv rate-distortion function for memoryless continuous sources, when the correlation between the sources is modeled via an additive noise channel. Modeling the distribution of the correlation noise via a Gaussian mixture, with discrete or continuous mixing variable, provides a unified signal model able to describe a wide class of distributions, useful in the context of practical applications. The Wyner-Ziv rate-distortion function associated with this signal model cannot, in general, be obtained in analytical form. This paper contributes a method for its analysis, by providing computable upper and lower bounds.

This paper considers the problem of universal lossless source coding with side information at the decoder only. The correlation channel between the source and the side information is unknown and belongs to a class parametrized by some unknown parameter vector. A complete coding scheme is proposed that works well for any distribution in the class. At the encoder, the proposed scheme encompasses the determination of the coding rate and the design of the encoding process. Both contributions result from the information-theoretical compression bounds of universal lossless source coding with side information. Then a novel decoder is proposed that takes into account the available information regarding the class. The proposed scheme avoids the use of a feedback channel or the transmission of a learning sequence, which both would result in a rate increase at finite length.

This paper considers the problem of lossy source coding with side information at the decoder only, for Gaussian sources, when the joint statistics of the sources are partly unknown. We propose a practical universal coding scheme based on scalar quantization and nonbinary LDPC codes, which avoids the binarization of the quantized coefficients. We first explain how to choose the rate and to construct the LDPC coding matrix. Then, a decoding algorithm that jointly estimates the source sequence and the joint statistics of the sources is proposed. The proposed coding scheme suffers no loss compared to the practical coding scheme with same rate but known variance.

This paper proposes an improved cross-layer control mechanism to efficiently stream videos to mobile users over an LTE network. A proxy-based filtering algorithm among scalable layers is considered to decide the number of SVC layers to transmit for each frame according to the communication conditions and to the class to which the video belongs to. The problem is cast in the context of Markov Decision Processes which allow the design of foresighted policies maximizing some long-term accumulated reward. Optimal actions to apply to the system are obtained by reinforcement learning. The proposed solution is implemented in an LTE simulation platform. Experiments show the performance of the proposed class-based layer filtering algorithm for a single video transmission and its robustness to content changes.

This paper addresses the design of joint source-channel variable-length codes with maximal free distance for given codeword lengths. While previous design methods are mainly based on bounds on the free distance of the code, the proposed algorithm exploits an exact characterization of the free distance. The code optimization is cast in the framework of mixed-integer linear programming and allows to tackle practical alphabet sizes in reasonable computing time.

This paper focuses on the performance of a Wyner-Ziv coding scheme for which the correlation between the source and the side information is modeled by a hidden Markov model with Gaussian emission. Such a signal model takes the memory of the correlation into account and is hence able to describe the bursty nature of the correlation between sources in applications such as sensor networks, video coding etc. This paper provides bounds on the rate-distortion performance of a Wyner-Ziv coding scheme for such model. It proposes a practical coding scheme able to exploit the memory in the correlation. Finally, the contribution to each part of the coding and decoding scheme is analysed.

Oversampled filter banks perform a subband decomposition with redundancy representation. This redundancy has been shown to be useful to combat channel impairments, when the subbands are transmitted over a wireless channel, as well as quantization noise. This paper describes an implementation of the maximum a posteriori and the minimum mean-square error (MMSE) estimators of the input signal from the noisy quantized subbands obtained at the output of some transmission channel. The relations between the input samples and the noisy subband samples are described using a factor graph. Belief propagation is then applied to get the posterior marginals of the input samples. The experimental results show that when the channel is clear, a linear MMSE estimate performs quite well but the proposed approaches perform significantly better than a reconstruction using the linear MMSE estimator when the channel is noisy: a gain in terms of channel SNR of more than 2 dB is observed.

This paper addresses the problem of efficient video streaming to mobile users. A cross-layer optimization of various parameters of the coding and transmission chain (coding parameters, buffer management, MAC-layer management) is performed to account for the time-varying nature of the characteristics of the transmitted contents and of the wireless channel. The problem is cast in the framework of Markov Decision Processes (MDP). This formalism provides efficient tools to compute a foresighted control policy maximizing some long-term discounted sum of rewards linked to the video quality received by the user. Experimental results illustrate the benefits in terms of average PSNR of this approach compared to a short-term (myopic) policy. The robustness of the proposed control policy to variations of the transmitted contents is also illustrated.

This paper proposes a quality-fair video delivery system able to transmit several encoded video streams to mobile users sharing some wireless resource. Video quality fairness, as well as similar delivery delay is targeted among streams. The proposed control system is implemented within some aggregator located near the bottleneck of the network.

This paper considers a multicast scenario and compares the average reception quality obtained when combining multiple description coding (MDC) and network coding (NC). Plain (single description) network coding (NC-SDC) serves as reference. In the considered scenario, a single source is multicast to several receivers with various channel conditions. Contrary to a NC-SDC scheme, unable to recover the coded packets when not enough combinations of packets have been received, NC of MDC packets allows a more progressive quality improvement with the number of received packets, and a reduction of the effect of the quantization noise when MDC is performed via frame expansion before quantization. Considering a probability distribution for the bit transition probability during transmission to any user in the multicast group, the expected signal-to-noise ratio is evaluated. Performance comparisons are made for various error distributions, field sizes, and MDC methods (via frame expansion and correlating transform).

Channel decoding makes use of redundancy in the coded bits. However, any redundancy in the information bitstream can also improve the decoding process. This paper shows that a careful examination of communication standards (including the headers added by the network layers) exhibits some fields which can be considered as redundant, either based on already received packets, or on the standard. The efficient use of this knowledge in the channel decoding process is denoted as protocol-assisted channel decoding. Assuming perfect synchronization and available channel state information, the proposed method applied on 802.11a PHY and MAC layers provides a substantial link budget improvement without modifying the standard, while the introduction of an additional interleaver provides additional bit error rate improvements.

In many communication standards, several variable length frames generated by some source coder may be aggregated at a given layer of the protocol stack in the same burst to be transmitted. This decreases the signalization overhead and increases the throughput. However, after a transmission over a noisy channel, Frame Synchronization (FS), i.e., recovery of the aggregated frames, may become difficult due to errors affecting the bursts. This paper proposes several robust FS methods making use of the redundancy present in the protocol stack combined with channel soft information. A trellis-based FS algorithm is proposed first. Its efficiency is obtained at the cost of a large delay, since the whole burst must be available before beginning the processing, which might not be possible in some applications. Thus, a low-delay and reduced-complexity Sliding Window-based variant is introduced. Second, an improved version of an on-the-fly three-state automaton for FS is proposed. Bayesian hypothesis testing is performed to retrieve the correct FS. These methods are compared in the context of the WiMAX MAC layer when bursts are transmitted over Rayleigh fading channels.

This paper proposes branch-and-prune algorithms for searching prefix-free joint source-channel codebooks with maximal free distance for given codeword lengths. For that purpose, it introduces improved techniques to bound the free distance of variable-length codes.

In this paper, the problem of scalable video delivery over a time-varying wireless channel is considered. Packet scheduling and buffer management in both Application and Medium Access Control (MAC) layers are jointly considered. Various levels of knowledge of the state of the channel are considered. The control is performed via scalable layer filtering (some scalability layers may be dropped). In all cases, the problem is cast in the context of Markov Decision Processes which allows the design of foresighted policies maximizing some long-term reward. Without channel state observation, the control has to rely on the observation of the level of the MAC buffer only. Experimental results show that even with a lack of knowledge of the channel state, the foresighted control policy provides only a moderate loss in received video quality.

Frame Synchronization (FS) is required in several communication standards in order to recover the individual frames that have been aggregated in a burst. This paper proposes a low-delay and reduced-complexity Sliding Trellis (ST)-based FS technique, compared to our previously proposed trellis-based FS method. Each burst is divided into overlapping windows in which FS is performed. Useful information is propagated from one window to the next. The proposed method makes use of soft information provided by the channel, but also of all sources of redundancy present in the protocol stack. An illustration of our ST-based approach for the WiMAX Media Access Control (MAC) layer is provided. When FS is performed on bursts transmitted over Rayleigh fading channel, the ST-based approach reduces the FS latency and complexity at the cost of a very small performance degradation compared to our full complexity trellis-based FS and outperforms state-of-the-art FS techniques.

This paper considers the optimization of a class of joint source-channel codes described by finite-state encoders (FSEs) generating variable-length codes. It focuses on FSEs associated to joint source-channel integer arithmetic codes, which are uniquely decodable codes by design. An efficient method for computing the free distance of such codes using Dijkstra's algorithm is proposed. To facilitate the search for codes with good distance properties, FSEs are organized within a tree structure, which allows the use of efficient branch-and-prune techniques avoiding a search of the whole tree.

Statistical multiplexing aims at transmitting several variable bit rate (VBR) encoded video streams over a bandlimited channel. Rate-distortion (RD) models for the encoded streams are often used to control the video encoders. As discrepancies frequently occur between the actual RD characteristics and their models, buffers are placed at the output of each coder to facilitate regulation. In this paper, a statistical multiplexer is proposed where video coders and buffers are controlled in a closed loop. First, a predictive joint rate controller accounting for maximum distortion, fairness, and smoothness constraints is considered. Second, all buffers are controlled simultaneously to limit deviations from a reference buffer occupancy to prevent buffer under and overflow. The main idea is to update the encoding rate for each encoding unit according to the average level of the buffers, to maximize the quality of each program. Simulation results show that the proposed scheme yields a smooth and fair video quality among programs thanks to the predictive control and allows an efficient use of the available bandwidth.

The control of the trade-off between encoding rate and quality of compressed video is a challenging task. This control requires efficient rate and distortion (R-D) models, able to describe accurately the behavior of the compressed video. R-D models should account for the dependencies induced by the choice of frame-varying quantization parameters (QP). This paper proposes a dependent R-D model involving two independent regime-switching autoregressive (IRSAR) models. Experimental results show that the proposed model is able to represent accurately the distortion dependency between frames. For the part of the rate due to the texture, the model fits experimental curves reasonably well. The model has to be completed to account also for the part of the rate due to motion vectors and signalization.

This paper presents a bandwidth-efficient method for improved decoding of critical data generated by the MPEG-4 AAC audio coder when encoded bitstreams are transmitted over noisy channels. The critical data of each encoded frame is estimated using the redundancy due to the correlation between successive frame headers and using an optional CRC as an error-correcting code. Simulation results for an AWGN channel show a substantial link budget improvement (more than 5 dB) compared to a classical hard-decoding method. Once critical data are efficiently estimated, the work of previously proposed joint source-channel decoding techniques to recover the remaining parts of the MPEG4 AAC frames is significantly facilitated.

This paper proposes a guaranteed robust bounded-error distributed estimation algorithm. It may be employed to perform parameter estimation from data collected in a network of wireless sensors. The algorithm is robust to an arbitrary number of outliers. Using interval analysis, one is able, provided that the network is connected, to evaluate at each sensor, an outer approximation of the set of all parameter values which are consistent with a given number of measurements, and with noise bounds. An application to a robust distributed source localization problem is considered.

Recently, Joint Source-Channel Decoding (JSCD) techniques have been proposed to improve the reception of multimedia contents transmitted over error-prone channels. These techniques take advantage of the redundancy left by the source coder and of bit reliability measures (soft information) provided by channel decoders to correct transmission errors. To be put at work, protocol stacks have to be made permeable to transmission errors in order to allow soft information to reach the upper protocol layers. For that purpose, headers have to be reliably estimated at each protocol layer. First results have been obtained for lower protocol layers (PHY and MAC) protected by CRCs. The aim of this paper is to extend these results to upper protocol layers (IP and UDP-lite) protected by checksums. As for CRCs, trellis-based decoding techniques may be employed for data protected by checksums. Nevertheless, specific tools have been proposed in this paper to reach a complexity-efficiency trade-off.

In this paper, we propose a joint source-channel (JSC) decoding scheme for 3D ESCOT-based video coders, such as Vidwav. The embedded bitstream generated by such coders is very sensitive to transmission errors unavoidable on wireless channels. The proposed JSC decoder employs the residual redundancy left in the bitstream by the source coder combined with bit reliability information provided by the channel or channel decoder to correct transmission errors. When considering an AWGN channel, the performance gains are in average 4 dB in terms of PSNR of the reconstructed frames, and 0.7 dB in terms of channel SNR. When considering individual frames, the obtained gain is up to 15 dB in PSNR.

This paper addresses the problem of video on demand delivery over a time-varying wireless channel. Packet scheduling and buffer management are jointly considered for scalable video transmission to adapt to the changing channel conditions. A proxy-based filtering algorithm among scalable layers is considered to maximize the decoded video quality at the receiver side while keeping a minimum playback margin. This problem is cast in the context of Markov Decision Processes which allows the design of foresighted policies maximizing some long-term reward. Experimental results illustrate the benefit of this approach compared to a shortterm policy in term of average PSNR improvement.

Joint source-channel decoding (JSCD) exploits residual redundancy in compressed bitstreams to improve the robustness to transmission errors of multimedia coding schemes. This paper proposes an architecture to introduce some additional side information in compressed streams to help JSCD. This architecture exploits a reference decoder already present or introduced at the encoder side. An application to the robust decoding of 3D-ESCOT encoded bitstreams generated within the Vidwav video coder is presented. The layered bitstream generated by this encoder allows SNR scalability, and moreover, when processed by a JSCD, provides increased robustness to transmission errors compared with a single layered bitstream.

This paper considers the optimization of Error-Correcting Variable-Length Codes (EC-VLC), which are a class of joint-source channel codes. The aim is to find a prefix-free codebook with the largest possible free distance for a given set of codeword lengths, ℓ = (ℓ1, ℓ2, ..., ℓM). The proposed approach consists in ordering all possible codebooks associated to ℓ on a tree, and then to apply an efficient branch-and-prune algorithm to find a codebook with maximal free distance. Three methods for building the tree of codebooks are presented and their efficiency is compared.

Video transmission on wireless links usually requires some frame aggregation, so that the overhead due to the headers is not a too large percentage of the bit-stream. In such a situation, header error detection is essential, because any error in the header may cause a loss of several consecutive frames. The contributions of this paper are (i) an improved reception of the length field of the header and (ii) an improved Frame Synchronization (FS) algorithm for aggregated frames. The FS algorithm has the following characteristics: (i) it makes use of the implicit redundancies which are present in the headers, thus resulting in an efficient synchronization of the variable-length frames; (ii) Bayesian hypothesis testing is used to estimate the correct synchronization; (iii) the proposed algorithm performs estimation on-the-fly and does not require reading the whole bit-stream. A comparative performance evaluation with respect to previously proposed algorithms is provided in the context of WiMAX MAC.

This paper presents an enhanced permeable layer mechanism useful for highly robust packetized multimedia transmission. Packet header recovery at various protocol layers using MAP estimation is the cornerstone of the proposed solution. The inherently available intra-layer and inter-layer header correlation proves to be very effective in selecting a reduced set of possible header configurations for further processing. The best candidate is then obtained through soft decoding of CRC protected data and CRC redundancy information itself. Simulation results for WiFi transmission using DBPSK modulated signals over AWGN channels show a substantial (4 to 12 dB) link budget improvement over classical hard decision procedures. We also introduce a sub-optimal and hardware realizable version of the proposed algorithm.

The class of variable-length finite-state joint sourcechannel codes is defined and a polynomial complexity algorithm for the evaluation of their distance spectrum presented. Issues in truncating the spectrum to a finite number of (possibly approximate) terms are discussed and illustrated by experimental results.

This paper presents a MAP estimation method allowing the robust decoding of compressed video streams by exploiting the bitstream structure (Le., information about the source, related to variable-length codes and source characteristics) together with the knowledge of the MAC layer CRC (here considered as additional redundancy on the MAC packet). This method is implemented via a sequential decoding algorithm in which the branch selection metric in the decoding trellis incorporates a CRC-dependent factor, and the paths which are not compatible with the source constraints are pruned. A first implementation of the proposed algorithm performs exact computations of the metrics, and is thus computationally expensive. Therefore, we also introduce a suboptimal (with tunable complexity) version of the proposed metric computation. This technique is then applied to the robust decoding of sequences encoded using the H.264/AVC standard based on CAVLC and transmitted using a WiFi-like packet structure. Significant link budget improvement results are demonstrated for BPSK modulated signals sent over AWGN channels, even in the presence of channel coding.

This paper presents a practical coding scheme for the direct symmetric multiterminal source coding problem with remote source, when the noise between the remote source and the observations is Gaussian-Bernoulli-Gaussian. The idea behind the design is to take advantage from the observed symbols being in the real field, in order to perform low-dimensional compressed sensing. The coding scheme is based on channel coding techniques involving BCH codes over the real field. Simulations with respect to the rate distortion performance are provided, along with insights about optimization issues. Robustness to variations of the probability of impulse is also investigated. Perspectives about the application to the CEO problem in presence of Gaussian-Bernoulli-Gaussian correlation noise between the remote source and the observations conclude the paper.

The problem of efficient video transmission over noisy channels involves high compression rates and robustness to channel errors. In the framework of multiple description coding (MDC), we focus on the direct estimation of the source from two noisy descriptions, without trying to estimate the single descriptions. The challenge is to reconstruct a central signal with distortion as small as possible using the knowledge of the two noisy descriptions. We propose in this paper a maximum a posteriori (MAP) estimator for the decoding of the central description, using the knowledge of the probability density function (pdf) of the different subband descriptions. The balanced MDC scheme used for application is a scan-based wavelet transform video coding scheme, and includes an efficient bit allocation procedure that dispatches the source video redundancy between the different descriptions, depending on the characteristics of the channel. Simulation results show a good robustness of the proposed decoding scheme against transmission errors, with an improvement of 2 dB in PSNR compared to a maximum likelihood (ML) technique.

This paper presents an improved sequential MAP estimator to be used as a joint source-channel decoding technique for CABAC encoded data. The decoding process is compatible with realistic implementations of CABAC in standards like H.264, i.e, handling adaptive probabilities, context modeling and integer arithmetic coding. Soft-input decoding is obtained using an improved sequential decoding technique, which allows to obtain a tradeoff between complexity and efficiency. The algorithms are simulated in a context reminiscent of H264. Error detection is realized by exploiting on one side the properties of the binarization scheme and on the other side the redundancy left in the code string. As a result, the CABAC compression efficiency is preserved and no additional redundancy is introduced in the bit stream. Simulation results outline the efficiency of the proposed techniques for encoded data sent over AWGN and UMTS-OFDM channels.

Practical schemes for distributed video coding with side information at the decoder need to consider non-standard correlation models in order to take non-stationarities into account. In this paper we introduce two correlation models for Gaussian sources, the Gaussian- Bernoulli-Gaussian (GBG) and the Gaussian-Erasure (GE) models, and evaluate lower and upper bounds on their rate-distortion functions. Provided that the probability of impulse noise or of erasures remains small, these bounds remain close to the rate-distortion function for Gaussian correlation. Two practical schemes for the GE correlation model are also presented, with performance about 1.5 dB away from the lower bound.

This paper addresses the problem of planning paths for a vehicle that are guaranteed to be safe in the presence of bounded uncertainty on the model of the vehicle, its sensors, and on the initial configuration. A new conceptual path planner based on rapidly-exploring random trees is proposed, which uses a set description of the uncertain configurations. A practical implementation of this path planner based on interval analysis is then described. It provides paths that could be safely followed by the vehicle during navigation, whatever the values taken by the uncertain quantities.

This paper defines a retransmission scheme for transmitting video data which allows the use of robust decoding (such as joint source and channel decoding) jointly with acknowledgement procedures. Conventional video communications use CRC-based retransmissions to obtain a nominal quality of the video (defined by the source coder). A lot of work has also been done on robust video transmission, which aims at providing a video of the best possible quality, for a given channel. However, robust techniques are not compatible with CRC-based retransmissions, and the quality of the received signal cannot really be controlled. As a result, the throughput is the best possible one, at the cost of a reduced quality. The scheme we define allows the use of robust decoding together with retransmissions, thus improving the throughput of the system while trying to keep the nominal quality. As a result, the proposed scheme is compatible with most actual mechanisms. The method is based on an evaluation of the quality of the robust estimate, followed by an hypothesis testing to decide whether a retransmission is necessary or not. Simulations evaluate the quality and the average number of retransmissions.

This paper presents a MAP estimation method of CAVLC (Context Adaptive Variable Length Coding) encoded sequences for robust decoding of H.264/AVC video stream. A sequential decoding algorithm jointly exploiting the residual source information (related to VLC codes and source characteristics) along with the MAC layer CRC redundancy in the transmission scheme is proposed. The branch selection metric in the decoding trellis incorporates the usually considered APP weighted by a CRC dependent factor. We also introduce a sub-optimal but hardware realizable version of the proposed algorithm. Significant link budget improvement results have been demonstrated for transmission schemes using BPSK modulated signals sent over AWGN channels.

In this paper, a new still image coding scheme is presented. In contrast with standard tandem coding schemes, where the redundancy is introduced after source coding, it is introduced before source coding using real BCH codes. A joint channel model is first presented. The model corresponds to a memoryless mixture of Gaussian and Bernoulli-Gaussian noise. It may represent the source coder, the channel coder, the physical channel, and their corresponding decoder. Decoding algorithms are derived from this channel model and compared to a state-of-art real BCH decoding scheme. A further comparison with two reference tandem coding schemes and the proposed joint coding scheme for the robust transmission of still images has been presented. When the tandem scheme is not accurately tuned, the joint coding scheme outperforms the tandem scheme in all situations. Compared to a tandem scheme well tuned for a given channel situation, the joint coding scheme shows an increased robustness as the channel conditions worsen. The soft performance degradation observed when the channel worsens gives an additional advantage to the joint source-channel coding scheme for fading channels, since a reconstruction with moderate quality may be still possible, even if the channel is in a deep fade.

This paper presents a coding scheme suitable for transmission of multimedia data over mixed Internet and wireless channels. To resist packet losses and provide good compression performance, an entropy-constrained multiple description trellis-coded quantizer is combined with a variable-length code. Furthermore, to be robust to transmission errors, an iterative decoding scheme is developed, which exploits the redundancy between the generated descriptions. Experimental results show the performance of this scheme in terms of efficiency and robustness.

Robust video coding has attracted increasing attention during the past few years. This paper proposes a joint source channel coding scheme able to resist transmission errors over mixed Internet-wireless channels. It involves motion-compensated oversampled filterbanks (OFBs). The redundancy introduced by the overcomplete representation in signals at the output of OFBs is employed for error correction of the motion compensated frames. The errors may be due to the wireless part of the channel (random noise), but also to the Internet part (packet losses). The performance of the proposed approach is illustrated for compressed streams transmitted through a packet erasure channel with an averaged packet loss of 6.25% followed by a binary symmetric channel with a crossover probability of 10-2

This paper presents a MAP estimator for CABAC encoded data transmitted through a noisy channel. The decoding process has two characteristics (i) it provides an exact result, without approximation (ii) it is compatible with realistic implementations of CABAC in standards like H.264, i.e. taking into account finite precision problems and handling adaptive probabilities and context modeling. Simulation results outline the efficiency of the proposed method when applied in AWGN and UMTS-OFDM frameworks

This paper is about source localization in a network of sensors from readings of signal strength. Contrary to previously published results, the source signal strength and path loss exponent are not assumed known a priori. The measurement errors are assumed bounded, with known bounds. The problem is then solved both in a centralized and in a distributed context using bounded-error parameter estimation techniques and interval analysis. Simulation results with realistic measurements are provided

In this paper, we present a joint source-channel decoding technique based on exact MAP estimation for data encoded by CABAC (context-based adaptive binary arithmetic coding) in standards like H.264/AVC. Soft decoding is put at work using an improved sequential decoding technique, achieving a trade-off between complexity and efficiency of the proposed algorithms. Error detection is realized by exploiting the binarization scheme and redundancy left in the code string, so that CABAC compression efficiency is preserved and no additional redundancy is compulsory

In joint source-channel (JSC) schemes based on arithmetic coding (AC), additional redundancy may be introduced in order to reduce transmission errors. The purpose of this work is to provide analytical tools to predict and evaluate the effectiveness of that redundancy. Integer binary AC is modeled by a reduced-state automaton in order to obtain a bit-clock trellis of the AC. Considering AC as a trellis code, distance spectra are then derived. In particular, an algorithm to compute the free distance of an arithmetic code is proposed. The obtained code properties allow to compute upper bounds on both bit error and symbol error probabilities and thus provide an objective criterion to analyze the behavior of JSCAC schemes when used on noisy channels

Due to its high compression efficiency, the H.264 video coder is very sensitive to impairments due to transmission over noisy channels. Most error resilience/concealment techniques provided in the H.264 standard were dealing with packet losses. In wireless environments, the proportion of corrupted packets (and thus considered as lost) may become very high. This paper shows that the H.264 decoder may be seen as a parity check decoder able to detect erroneous packets. Combined with soft estimation techniques, it allows to correct transmission errors and to reduce significantly the number of packets deemed lost. The proposed solution is compatible with the error-resilience features of H.264

Many source coding standards (JPEG, H263+, H264), rely heavily on entropy coding with variable-length codes (VLC). However, bitstreams made of VLC-encoded data are particularly sensitive to transmission errors. Recent results tend to use knowledge of the VLC structure in order to perform an efficient decoding of the bitstream. These techniques use a trellis describing the structure of the VLC codebook and assume that some a priori information is available at decoder side. Significant improvements, compared with prefix decoding of bitstreams are achieved. However, the complexity of these techniques may become intractable when realistic VLC codebooks are considered. This paper presents an algorithm for compacting VLC tables. The codewords are grouped into a minimum number of classes. Decoding algorithms may then work on a reduced number of classes, instead of working on the whole set of codewords. A proof of optimality is provided for the VLC table-compaction algorithm. The algorithm is applied to the H263+ VLC codebook and merges the 204 codewords into 25 classes. The resulting compact tables are shown to be exactly equivalent to the initial ones when used with hard decoding algorithms. The properties of the associated soft decoding algorithms using these compact tables are also evaluated.

During the past few years, much effort has been devoted for efficient coding of video. However, this high coding efficiency results in an increased sensitivity to transmission errors. This is the motivation for developing techniques allowing us altogether to transmit video sequences efficiently and to be robust to the transmission errors. This paper proposes a joint source-channel coding scheme using motion-compensated oversampled filterbanks (OFB). Previous work for robust transmission using OFB have concentrated on still images. Here, OFB are combined with motion compensation techniques. The redundancy introduced by the overcomplete representation is employed for error correction of the motion compensated frames. Experiments have shown that performance is acceptable even when the compressed stream is transmitted through a binary symmetric channel with crossover probability of 10/sup -2/.

The soft decoding of variable-length encoded texture data generated, e.g., by video coders such as H.263+ and sent over a packetized network (Internet or mixed Internet radio-mobile) is considered here. Existing soft decoding techniques usually make use of either the number of bits in a block or the number of symbols in a block. Thus, this side information has to be transmitted. This paper describes an algorithm making use only of information available in the packetized bitstream, and is thus compatible with existing standards. Simulations illustrating the efficiency of the proposed decoding procedure are provided.

Estimating the configuration of a vehicle is crucial for navigation. The most classical approaches are Kalman filtering and Bayesian localization, often implemented via particle filtering. This paper reports on-going experimentation with an attractive alternative approach recently developed and based on interval analysis. Contrary to classical extended Kalman filtering, this approach allows global localization, and contrary to Bayesian localization it provides guaranteed results in the sense that a set is computed that contains all of the configurations that are consistent with the data and hypotheses. The approach is particularly robust to outliers.

Transmitting video streams on channels impaired with transmission errors is a very demanding task, mainly when images are predicted from previous ones. In this case, errors on motion vectors can be very harmful. In order to overcome this problem, this paper presents a modified H263+ scheme without motion vector transmission. This is obtained by reestimating these motion vectors at the receiver, based on properties of frame expansions. This procedure is obtained at the cost of an increased bit rate, but shows that robust (and efficient) transmission can indeed be obtained in conjunction with image prediction.

With the increasing use of multimedia technologies, video coding requires higher performance as well as new features. To address this need, the latest ITU-T video coding standard, H.264, has been developed. The motion compensation algorithm is the key of the improvements over H.263+. This paper proposes an H.264-based algorithm which does not require the motion vectors to be transmitted. It extends previous results obtained in the H.263+ context. The main purpose of this work is to check whether our procedure of reestimating the motion vectors at the receiver is compatible with the initial H.264 scheme efficiency. It is shown that this property is obtained at some cost in terms of bit rate for comparable PSNR, but that our new H.264-based scheme has about the same performance level as the plain H.263+ coder. This is essentially a preliminary work, which shows that the most sensitive part or the bit stream can be removed, thus allowing new robust coders to be developed.

An analogy between oversampled filterbanks and channel codes is considered. Parity-check polynomial matrices and syndromes can be defined. Techniques for detecting and correcting impulse noise are provided. Their performances are illustrated on an example.

When reliable prior bounds on the acceptable errors between the data and corresponding model outputs are available, bounded-error estimation techniques make it possible to characterize the set of all acceptable parameter vectors in a guaranteed way, even when the model is nonlinear and the number of data points small. However, when the data may contain outliers, i.e., data points for which these bounds should be violated, this set may turn out to be empty, or at least unrealistically small. The outlier minimal number estimator (OMNE) has been designed to deal with such a situation, by minimizing the number of data points considered as outliers. OMNE has been shown in previous papers to be remarkably robust, even to a majority of outliers. Up to now, it was implemented by random scanning, so its results could not be guaranteed. In this paper, a new algorithm based on set inversion via interval analysis provides a guaranteed OMNE, which is applied to the initial localization of an actual robot in a partially known two-dimensional (2-D) environment. The difficult problems of associating range data to landmarks of the environment and of detecting potential outliers are solved as byproducts of the procedure.