Mobile Extended Reality (XR) communication services offer unparalleled opportunities across various verticals, but present specific challenges due to their high throughput requirements, often in the hundreds of Mbps, and stringent end-to-end delay constraints, usually as low as a few milliseconds. To address these challenges, we propose the eXtended Reality-Oriented Orchestration of Access Resources (X-OAR), designed to support XR applications, by leveraging next-generation network access technologies, even in densely populated environments. X-OAR integrates cooperatively scheduled radio access network resources with edge computing capabilities. X-OAR complies with the stringent delay requirements defined by 3GPP for XR quality of experience through cooperative joint edge and radio resources scheduling. We formulate the delay minimization problem using a graph-based approach and introduce a greedy algorithm that reduces orchestration complexity and prior knowledge of user subscription data. Numerical simulations demonstrate that X-OAR’s cooperative scheduling outperforms state-of-the-art solutions, delivering superior XR quality of experience. Furthermore, X-OAR paves the way for future research on extending orchestration to application-layer strategies and resource-aware charging policies.

Several components in the design of next-generation networks, including user profiling and network slicing, rely on accurate models of traffic load. In this context, recent studies have focused on various video traffic categories, while traffic associated with extended reality (XR) services has received limited attention. This paper introduces a novel empirical model for 3D XR traffic, developed by encoding real Point Clouds using a standard-compliant codec, and able to account for the dynamic of service sessions and user behaviors over an entire session. Our methodology encompasses multiple temporal scales, ranging from milliseconds to minutes, to account for different phenomena related to both user behaviour and encoder settings. Initially, we investigate the packet size distribution at the time scale of a semantic unit, corresponding to the encoding of a single point cloud. We verify that it can be effectively represented by a heavy-tailed Gamma distribution. Then, we illustrate how this insight can be leveraged to model application-layer phenomena. Specifically, we demonstrate the applicability of a general semi-hidden Markov model to capture both the temporal dynamics of service sessions and user behaviors. We provide results in terms of comparison of the empirical and fitting traffic distributions, based on quantile to quantile analysis and statistical tests. We also show how the model can be trained on real data and we provide a pseudo-code demonstrating the model application within a network simulator.

This research presents a comprehensive investigation into subjective video shakiness assessment. A collection of 30 shaky videos was gathered, covering relevant categories such as climbing, driving, large parallax, rotation, running, and walking, with different scenes and levels of shakiness. A pairwise comparison (PWC) was conducted, involving human observers who evaluated the perceived quality of shaky videos, and the results have been converted into quality scores using the Just-Objectionable-Differences (JOD) scaling method. The shakiness assessment framework was proved effective by correlations between objective metrics and subjective judgments, and it can serve as a benchmark for future advancements in the field, fostering improvements in video stabilization technologies and applications. The complete dataset is made publicly available through the following link:Shakiness-QuAD

Water scarcity is nowadays a critical global concern and an efficient management of water resources is paramount. This paper presents an original approach for monitoring Water Distribution Systems (WDSs) through Internet of Things (IoT) that involves the integration of multiple sensors placed across the distribution network to accurately measure water flow. To enhance energy efficiency for green monitoring and communication process, we harness the power of graph theory and graph signal processing to represent in a tunable and accurate way the water flow and simultaneously minimize the number of IoT sensors communicating those measurements. We propose a graph model where water flow is represented as signal on graph and we introduce an algorithm, named GraphSmart, designed to reconstruct the graph signal when certain measurements are unknown or missing. Our framework is applied on a synthetic realistic environment within the context of LoRaWAN (Long Range Wide Area Network), an infrastructure and protocol designed for ultra-low-power IoT devices. Our findings show that GraphSmart significantly reduces energy consumption while ensuring precise flow estimation. Our research demonstrates high potential for energy-efficient and accurate water flow monitoring, paving the way to improve the management of WDSs and enabling water operators to address water scarcity challenges.

Real-world networks are typically described in terms of nodes, links, and communities, having signal values often associated with them. The aim of this letter is to introduce a novel Compound Markov random field model (Compound MRF, or CMRF) for signals defined over graphs, encompassing jointly signal values at nodes, edge weights, and community labels. The proposed CMRF generalizes Markovian models previously proposed in the literature, since it accounts for different kinds of interactions between communities and signal smoothness constraints. Finally, the proposed approach is applied to (joint) graph learning and signal recovery. Numerical results on synthetic and real data illustrate the competitive performance of our method with respect to other state-of-the-art approaches.

We investigate the problem of providing a video streaming service to mobile users in an heterogeneous cellular network composed of micro e-NodeBs (μeNBs) and macro e-NodeBs (MeNBs). More in detail, we target a cross-layer dynamic allocation of the bandwidth resources available over a set of μeNBs and one MeNB, with the goal of reducing the delay per chunk experienced by users. After formulating the optimal problem of minimizing the chunk delay, we detail the Cross LayEr Video stReaming (CLEVER) algorithm, to practically tackle it. CLEVER makes allocation decisions on the basis of information retrieved from the application layer as well as from lower layers. Results, obtained over two representative case studies, show that CLEVER is able to limit the chunk delay, while also reducing the amount of bandwidth reserved for offloaded users on the MeNB, as well as the number of offloaded users. In addition, we show that CLEVER performs clearly better than two selected reference algorithms, while being very close to a best bound. Finally, we show that our solution is able to achieve high fairness indexes and good levels of Quality of Experience (QoE).

We present a quality-selection policy for Quality of Experience (QoE) demanding video streaming in wireless networks. The proposed policy predicts the TCP throughput and adapts video segment requests in order to assure high QoE by taking into account the client buffer level. We introduce the concept of Affordable delivery Time (AT) and we design a buffer-based algorithm, hereafter referred to as Buffer-based lolyPOP (BPOP). The AT accounts for the ideal chunk download time which equals the chunk playout time, as well as for the client buffer status. In a nutshell, the buffer-based AT favors the download of higher quality video chunks when the client has more buffered data than a pre-established target buffer occupancy. Conversely, AT inhibits higher quality video chunks download when the buffer is below the targeted occupancy.

We propose a dynamic and cooperative approach to support the desired Quality of Experience (QoE) of the mobile users in a heterogeneous cellular network. Specifically, we focus on a scenario in which a mobile video streaming service is provided to users. We then develop a mobile streaming traffic micro-to-macro offloading scheme based on a cross layer approach, in which we exploit the knowledge of the video structure at a proxy side. Our results, obtained over a detailed case study, show that the proposed approach is able to enable high quality video streaming services that are otherwise not feasible. Moreover, even a moderate portion of the macro bandwidth (typically ranging between 15% and 25% of the total bandwidth) is already able to serve users that have been offloaded from the small cell. Finally, we show that, when the offloading is based on a cross-layer approach, the QoE experienced by users (e.g., in terms of delay) is higher than in the case in which the offloading decision is solely based on lower layer parameters like the channel quality.

The development of wireless personal area networks is one of the key challenges in the new frontier of communications. The standardization activities of the IEEE802.15.6 and IEEE802.15.4f workgroups along with the proposed 368-369 ECMA specifications open new roads to optimize performance in ultrawideband (UWB) communication links and services. In this perspective, new communications require high reliability, high throughput for multimedia content delivery, very low latency, and bit error rates are required. In this regard, this contribution deals with a turbo-like mechanism that, starting from an interference acquisition phase, proceeds with data-aided channel estimation and synchronization to data detection of ultrawideband impulse-radio (UWB-IR) terminals operating over broadband channels affected by multipath fading and in the presence of imperfect orthogonal access. The performance of the proposed transceiver has been evaluated in terms of both required number of pilot symbols for the channel estimation/data synchronization and bit error rate resulting from the channel estimation and interference analysis. Moreover, the performance related to system efficiency (i.e., data transmitted with respect to the whole information needed for estimation purposes) is evaluated by showing the balance between achieved transmission rate and bit error rate. Numerical comparisons have been performed as well as tests on real data and partial implementation of the proposed scheme.

We consider adaptive-rate scheduling for downlink unicast transmissions of video streams over cellular wireless networks. We study a service under which each mobile client receives requested video streams at a variable Quality of Experience (QoE) level, based on its experienced communication quality condition. We employ a proxy-video manager at the base station node. The manager classifies users into two groups, based on their reported experienced CQIs (Channel Quality Indicators). The manager intercepts a client's video stream request destined to the HTTP server, determines its group classification, and proceeds to transmit a properly encoded version of the requested stream. To effectively regulate inter- cell signal interference, we examine a number of different spectral reuse and fractional frequency reuse scheduling schemes. We calculate, for each scheduling and user group classification scheme, the average bandwidth per stream that is required to provide user groups with their targeted QoE levels. We demonstrate a user classification process that aims to optimize a utility metric based on the definition of a will-to-pay utility function. The derived optimal configuration of FFR based schemes are shown to significantly enhance the system's performance behavior.

This paper investigates the advantage of adopting a flexible resource control scheme when performing HTTP-based adaptive streaming across LTE systems. To guarantee video fluidity, mobile video streaming is known to require a large bandwidth overhead with respect to the net encoded video rate. The quality of a received video stream is impacted by variations in the size of the transmitted video packets (chunks), and by statistical fluctuations in the data rate at which the allocated downstream wireless channel operates. First, in considering an illustrative video scenario, we show that the chunk size distribution is heavy-tailed, and is well fit by a Gamma distribution. Second, we employ a HAS based proxy video manager and resource controller at the base station node. Based on the channel quality observed and reported by a mobile client, the manager selects the proper channel bandwidth and data rate levels at which to transmit the stream's chunks, in accordance with the selected encoded video rate and the configured Quality of Experience (QoE) level at which the user is targeted to receive the video stream.The communications data rate is also set to assure an acceptable low video reception stall probability. To illustrate the performance of such a dynamic bandwidth allocation scheme, we compare it with an operation that employs a stationary setting of the channel bandwidth, and we compute the gain achieved when such adaptations are performed at the base station node on a chunk by chunk basis. We show by analysis, and confirm by simulations, the improvements achieved in the system's performance behavior through the use of the adaptive resource allocation scheme.

Multiview video has recently emerged as a means to improve user experience in novel multimedia services. We propose a new stochastic model to characterize the traffic generated by a Multiview Video Coding (MVC) variable bit-rate source. To this aim, we resort to a Poisson hidden Markov model (P-HMM), in which the first (hidden) layer represents the evolution of the video activity and the second layer represents the frame sizes of the multiple encoded views. We propose a method for estimating the model parameters in long MVC sequences. We then present extensive numerical simulations assessing the model's ability to produce traffic with realistic characteristics for a general class of MVC sequences. We then extend our framework to network applications where we show that our model is able to accurately describe the sender and receiver buffers behavior in MVC transmission. Finally, we derive a model of user behavior for interactive view selection, which, in conjunction with our traffic model, is able to accurately predict actual network load in interactive multiview services.

This paper addresses the problem of texture images recovery from compressively sampled measurements. Texture images hardly present a sparse, or even compressible, representation in transformed domains (e.g. wavelet) and are therefore difficult to deal with in the Compressive Sampling (CS) framework. Herein, we resort to the recently defined Graph-based transform (GBT), formerly introduced for depth map coding, as a sparsifying transform for classes of textures sharing the similar spatial patterns. Since GBT proves to be a good candidate for compact representation of some classes of texture, we leverage it for CS texture recovery. To this aim, we resort to a modified version of a state-of-the-art recovery algorithm to reconstruct the texture representation in the GBT domain. Numerical simulation results show that this approach outperforms state-of-the-art CS recovery algorithms on texture images.

In compressive sensing, the Restricted Isometry Property is an analytical condition on the measurement matrix that assures reconstruction of a signal which is sparse either in the spatial or in a transformed domain given an undersampled measurements' set. In this paper, we demonstrate the RIP for a sparse, structured measurements matrix, referred to as Radon-like CS matrix. The sparse Radon-Like CS matrix favorably applies to real sensing problems, since it significantly reduces the energy/bandwidth cost of actually collecting each and every sensed values contributing to the CS measurements. Simulation results confirm the feasibility of field reconstruction from undersampled CS measurements set obtained using the Radon-Like CS matrix.

We develop an empirical model of the Multiview Video Coding (MVC) performance that can be used to identify and separate situations when MVC is beneficial from cases when its use is detrimental in wireless multimedia sensor networks (WMSN). The model predicts the compression performance of MVC as a function of the correlation between cameras with overlapping fields of view. We define the common sensed area (CSA) between different views, and emphasize that it depends not only on geometrical relationships among the relative positions of different cameras, but also on various object-related phenomena, e.g., occlusions and motion, and on low-level phenomena such as variations in illumination. With these premises, we first experimentally characterize the relationship between MVC compression gain (with respect to single view video coding) and the CSA between views. Our experiments are based on the H.264 MVC standard, and on a low-complexity estimator of the CSA that can be computed with low inter-node signaling overhead. Then, we propose a compact empirical model of the efficiency of MVC as a function of the CSA between views, and we validate the model with different multiview video sequences. Finally, we show how the model can be applied to typical scenarios in WMSN, i.e., to clustered or multi-hop topologies, and we show a few promising results of its application in the definition of cross-layer clustering and data aggregation procedures.

We experimentally characterize the compression efficiency of Multiview Video Coding (MVC) techniques in Wireless Multimedia Sensor network (WMSN) composed of multiple video cameras with possibly overlapping field of views. We derive an empirical model that predicts the compression efficiency as a function of the common sensed area (CSA) between different camera views. We show that the CSA depends not only on geometrical relationships among the relative positions of different cameras, but also on several object-related phenomena, e.g., occlusions and motion, and on low-level phenomena such as variations in illumination. We then apply the model to a WMSN, where we create clusters based on the CSA as estimated by exchanging local data. Based on this estimates, we form clusters and measure the resulting transmission rate. Numerical simulation results show that building clusters based on a CSA criterion can bring significant performance gains in terms of bandwidth efficiency. The herein presented promising results pave the way for clustering optimization taking into account different networks constraints and conditions.

We introduce a novel, both gain and signal-to-noise ratio independent, constellation unaware, blind frequency offset estimation procedure for QAM signals. Asymptotic performance analysis and numerical simulations show that the herein presented method outperforms a selected state of the art blind constellation unaware estimator, especially for cross constellations.

In this letter, we address the problem of estimating a parameter acting as a scale factor on the observations probability density function (pdf), i.e. a scale parameter. Histogram based Maximum Likelihood (ML) estimation of a scale parameter requires the evaluation of a discrete scale correlation. We show how ML estimation can be implemented by means of a computationally efficient Discrete Fourier Transform based procedure, when geometric histogram sampling is adopted. As a case study, we analyze a gain estimator for general QAM constellations. Simulation results and theoretical performance analysis show that the presented ML estimator outperforms selected state of the art estimators, approaching the Cramér-Rao Lower Bound (CRLB) for a wide range of SNR.

Multiview video is increasingly getting attention due to emerging applications such as 3DTV and immersive teleconferencing. In this paper, we present a non-stationary Hidden Markov Model (HMM) for characterizing the data rate of compressed multiview content. The states of the model correspond to different video activity levels and exhibit a Poisson state duration distribution. We derive a stable maximum likelihood algorithm for estimating the parameters of our multiview traffic model. Synthetic data generated by the model exhibits statistics that closely match those of actual multiview data. In addition, we demonstrate the high accuracy of the model in two multiview streaming applications by evaluating the frame loss rate of a constrained network buffer fed by actual and synthetic data.

In this paper we develop a strategy for visual relevancy evaluation using Rate Distortion analysis in the Circular Harmonic Functions (CHF) Domain. In the CHF domain, the different visual characteristics of the transformed frames - edges, lines, forks, crosses, etc - are emphasized. Resorting to this domain we analyze a new approach to evaluate the distortion due to an event of video data loss accounting for the visual relevance of individual lost data. In a real video streaming session, distortion measurements taking into account the visual impact of error events can be stored along with pre-coded video sequence so as to drive the strategies for ad-hoc error protection. Numerical simulations show that the approach herein proposed to evaluate the visual impact of loss events in the decoded sequences captures the visual distortion much better than commonly adopted mean square error criteria between the decoded and the original sequence.

In this paper we present theoretical performance analysis for a blind frequency offset estimator for cross quadrature amplitude modulated constellations. The estimator is based on applying a tentative frequency offset compensation by means of a nonlinear transformation of the received signal samples and on estimating an accumulation function in different angular windows. For perfect frequency offset compensation, the measurements are suitably clustered and their accumulation, named "constellation phase signature" (CPS), is a peaked function of the window orientation. Hence, the frequency offset estimator is selected by maximization of the peakness of the accumulation function. The performance analysis is shown to match the numerical simulations for medium to high values of SNR.

Fast and bit-saving video bit-rate switching is an important issue in video streaming systems on a time varying channel as the one offered by a wireless mesh network, or the one sensed during a vertical handover. The recent H.264 video coding standard supports the seamless switching among bitstreams coded at different bit-rates by means of suitably coded frames, named switching pictures. This work addresses the modelling of the traffic generated by a H.264 source performing bit-rate switching using SP frames. The H.264 source is modelled by a Markov chain where each state models the generation of an entire group of pictures (GOP), and is characterized by the kind of SP frame encoded in the GOP. Inter- frame correlation, typical of video sources, is suitably taken into account by the interstate dependence. The accuracy of the model is assessed by comparison of the cell loss rate of a fixed size buffer filled with a synthetic source according to the model herein proposed, with a state of the art AR model and with a real H.264 video codec.

In this work1we discuss a game theoretic approach to bitstream switching in video coding. Fast and bit-saving video bitstream switching is an important issue in video communication system on time varying channels. The most recent video coding standard, namely H.264, support the seamless switching among bitstreams coded at different bitrates by means of suitably coded frames, named Switching Pictures. Since the rate-distortion characteristics of switching frames differ from those of I and P frames, their location affect both the bit-rate and the quality of the coded sequence. In this work, we address the optimization of the SP frames location under an assigned bitrate budget. At this aim we restor to a game theoretic approach and we show that the optimal solution is met when the SP frames are assigned to the frame with the smallest innovation. Experimental results show the advantage in terms of both rate and distortion achieved by the optimized Switching frame insertion with respect to basic H264 coding.

We introduce a new blind phase offset estimator for general quadrature amplitude modulated (QAM) signals. The estimator is based on the computation of a suitable phase distribution that we call "signature". The signature is defined as the phase-dependent distribution of the received signal magnitude after the application of a nonlinear transformation. The signature of a QAM signal is constituted by a discrete number of pulses and it has good autocorrelation properties in the sense of maximum/side-lobe ratio. Since the effect of a phase offset is a cyclic shift of the signature, the phase offset can be estimated by searching for the maximum of the cyclic cross-correlation between the zero-phase signature of the expected constellation, and the signature calculated on the received signal. The resulting estimator is characterized by a low computational complexity and does not need gain control. The comparison shows that the presented estimator is asymptotical efficient and outperforms existing estimators for medium to high values of SNR, especially for complex constellations

In this paper, we present a texture classification procedure that makes use of a blind deconvolution approach. Specifically, the texture is modeled as the output of a linear system driven by a binary excitation. We show that features computed from one-dimensional slices extracted from the two-dimensional autocorrelation function (ACF) of the binary excitation allows representing the texture for rotation-invariant classification purposes. The two-dimensional classification problem is thus reconduced to a more simple one-dimensional one, which leads to a significant reduction of the classification procedure computational complexity.

In this contribution, we show how a hierarchical edge image analysis at macroblock, block and subblock levels allows identifying preminent directional image components. The analysis, performed in the Radon domain, selects the scale level at which the image features present a directional structure that can be better reconstructed by a directional spatial interpolation. When the directional information is known at the decoder side it can drive the spatial error concealment. The experimental results, referring to the case of H.264 coded video, show the significant improvement of the decoded video visual quality achievable by the described technique.

We discuss different architectures for error resilient video streaming. The architectures exploit a resilient video coding scheme, based on out-of-band transmission of suitable redundant information, that counteracts synchronization loss between the decoder and the bitstream due to transmission errors. We investigate unicast and multicast video streaming solutions and show a power/bandwidth efficient hybrid unicast/multicast architecture. Experimental results show the decoded video quality obtained by adopting the resilient streaming architecture in the case of H.264 coding.

This work describes how the introduction of redundancy by means of coding constraints known at the decoder side improves the error resilience of a video communication scheme. The decoder can exploit the a priori knowledge of the constraints to verify the integrity of the decoded bitstream. If the transmission errors cause the violation of the coding constraints, the decoder detects an error and recovers the synchronization with the bitstream. The improved detection capability results in better localization of the decoded frame areas affected by transmission errors, so allowing more accurate error concealment. The adoption of coding constraints does not require any feedback channel between the decoder and the coder since the decoder can be notified of the constraints during the initial negotiation phase. The syntax of the coder output is not affected and the coded bitstream remains standard compliant. Therefore, this technique is quite general and it is well suited to many transport schemes, either packet switched or circuit switched. Furthermore, it can be easily integrated with different error resilience tools. The improvement achievable in the decoded image quality using the coding constraints is here shown in the case of H.264 coded video.

This work addresses the multichannel Bussgang restoration algorithm for blind image deconvolution problems. The case of spatial correlated nonGaussian images is considered, and the structure of nonlinear minimum mean square error estimators, to be used in the Bussgang deconvolution algorithm, is discussed. In particular, it is conjectured that, for nonGaussian, spatially correlated random fields, a better nonlinear estimator is obtained in a suitable wavelet transformed domain. The wavelet transform here considered is the so-called circular harmonic wavelet (CHW), because of its characteristic property of capturing specific image features, such as edges, lines, forks, crosses, and so on. Experimental results pertaining to restoration of motion blurred natural images are also reported.

This work extends the Bussgang blind equalization algorithm to the multichannel case with application to image deconvolution problems. We address the restoration of images with poor spatial correlation as well as strongly correlated (natural) images. The spatial nonlinearity employed in the final estimation step of the Bussgang algorithm is developed according to the minimum mean square error criterion in the case of spatially uncorrelated images. For spatially correlated images, the nonlinearity design is rather conducted using a particular wavelet decomposition that, detecting lines, edges, and higher order structures, carries out a task analogous to those of the (preattentive) stage of the human visual system. Experimental results pertaining to restoration of motion blurred text images, out-of-focus spiky images, and blurred natural images are reported.