The convergence of the Internet of Things (IoT) and the metaverse creates systems where a Digital Twin (DT) sits between physical and virtual worlds. Keeping them in sync is hard when actions arrive at the same time with different priorities and delays. We present a DT template that supports two-way, asynchronous interactions and a stack of three policies: confirm matching intents, resolve conflicts with context (domain weights and timeliness), and rollback on invariant violations. We validate the approach in a smart building with DTs representing doors, windows, and lights operating within policy domains that include security, comfort, and energy efficiency. Without policies, conflicting windows already reach 33-56% with only two users for different request rates. With the policy stack, the share of conflicts resolved by policy increases with the reliability gap and can approach all the conflicts, reducing rollbacks. Finally, under asymmetric placement, by simulating the DT at different points in the network (e.g., edge close to physical devices and cloud close to the metaverse), rollbacks shift to the slower side but leave the overall resolution essentially unchanged.

It is well-known that, without access to application-layer parameters controlled by Over-The-Top (OTT) providers, Mobile Network Operators (MNOs) struggle to accurately predict customers’ Quality of Experience (QoE). While some previous proposals have suggested interaction between OTTs and MNOs, they have faced challenges in terms of practical implementation and limited application scenarios. This work aims to advance these solutions with two key contributions. First, following the Open Radio Access Network (O-RAN) architecture, we propose adding components that integrate a machine learning (ML)-based QoE prediction model, deployed by the MNO, into the O-RAN system. By establishing specific data-sharing interfaces between OTTs and MNOs, our approach helps MNOs overcome the limitations in updating their quality prediction modules. Second, we present a network-aware, ML-driven QoE prediction model that captures the relationship between the resulting QoE and various network parameters, such as signal-to-interference-noise ratio (SINR), channel quality indicator (CQI), network resource blocks (RBs), throughput, and device mobility. Among seven considered ML regressors, the Gradient Boosting (GB) achieved the highest QoE prediction performance in terms of R 2 (0.906) and RMSE (0.259).

Human Digital Twins (HDTs) are increasingly adopted across various domains, yet their application to network and service management remains limited. Nevertheless, HDTs offer significant potential for optimizing service configurations based on human behavior, preferences, and profiles. In this paper, we analyze the role of HDTs in network and service management, identifying key functionalities such as collaborative learning for user modelling, Quality of Experience (QoE) and emotion prediction, application personalization, and behavioral forecasting for network “what-if” analysis. We propose an architectural framework designed to monitor user status and generate a corresponding digital replica that interacts with other network components to enhance service delivery. Our solution integrates collaborative learning for QoE modelling and applies it to service optimization. By aggregating user data from multiple HDTs, the approach improves prediction accuracy and resource optimization. Extensive performance evaluations demonstrate that the proposed collaborative HDT framework enhances the final utility function that considers perceived quality and resource usage by 27% compared to non-collaborative methods.

The Internet of Things (IoT) is increasingly supporting the tourism sector by enabling adaptive services that enhance the visitor experience. Among emerging applications, itinerary planning has gained significant attention, leveraging IoT data to deliver personalized and adaptive routes. However, current systems show key limitations. Personalization often depends on static forms rather than adaptive learning; validation is usually restricted to simulations, and the few real implementations mostly rely on mobile apps, tools that tourists are reluctant to download and often abandon after limited use. To overcome these challenges, this paper proposes an itinerary planning system that integrates reinforcement learning for tourist profiling with a genetic algorithm for multi-objective optimization, implemented in a real-world scenario through a cloud infrastructure and delivered via an interactive totem that serves as the access point for tourists. Results confirm both the efficiency and scalability of the approach, showing that the system can be seamlessly extended to diverse urban contexts.

This paper presents the Music Event Emotion Tracking (MEET) Metaverse, which is one of the demo results produced within the FUN-Media project. The MEET Metaverse is a virtual disco where multiple users can join together through their avatars to enjoy musical events. The peculiar characteristic of the MEET Metaverse is that the emotions of participants are inferred from their facial expressions and speech, and are used to select the next song to be played in the disco based on the average emotional state of participants. Moreover, avatars’ facial poses are updated based on participants’ emotions, and realistic avatar animations are reproduced using a combination of motion retargeting and high-fidelity appearance modeling.

This paper presents the results of a subjective assessment investigating the impact of multiple factors (network, avatar, and player role) on the perceived Quality of Experience (QoE) in a multi-user collaborative virtual reality (VR) game. Forty test participants collaborated in pairs to complete a cooking VR game, wearing a Meta Quest Pro headset, under variable network conditions (no impairments or delayed network traffic), using diverse types of avatars (cartoon-style and humanoid), and interpreting different roles (teacher and student). A humanoid custom avatar has been implemented that replicates the user’s facial expressions and body movements to investigate whether the introduction of non-verbal emotional communication within a VR environment influences the perceived user experience. Quality-and emotion-related subjective metrics were rated by test participants at the end of each test session, and the computed results show that the cartoon-like avatar, being lightweight, provides the highest perceived QoE even when the network was impaired. On the other hand, while the QoE using the humanoid avatar lowers with the introduction of network distortions (because of the larger amount of data required to replicate the facial expressions), the ability to see the facial expressions of the partner prevents a greater reduction of user experience due to the network issues.

Accurate models are necessary for the continuous estimation of the Quality of Experience (QoE), which is crucial for delivering successful multimedia services to end-users. These models are developed from subjective test data, which very often provide only specific aspects of the experience, i.e., a partial view (PV). Each PV conveys a relationship between a specific set of influence factors and the perceived QoE, limiting the applicability of the derived model to other application scenarios not considered in the initial subjective tests. To extend the applicability of the developed models, this paper introduces a multi-view (MV) learning framework that enhances QoE prediction by integrating complementary information from multiple perspectives obtained from different subjective tests. We leverage a fully connected deep neural network with two initially independent branches and an intermediate fusion layer to combine insights from separate feature sets, improving predictive accuracy while preserving data privacy. Our model is trained on a synthetic data set derived from the TID2008 image database, ensuring a controlled yet representative evaluation environment. On the one hand, the results demonstrate that the MV technique outperforms all PV configurations. On the other hand, the MV approach achieves QoE estimation performance comparable to the single-view (SV) model, in which one single branch analyzes the full set of impact factors. In particular, the largest performance gain (6.15% – 142.69%) across most evaluation metrics occurred when the input data set is equally divided between the two separate views.

Point cloud quality assessment (PCQA) is a critical research area focused on evaluating the perceptual Quality of Experience (QoE) of point clouds to enhance visual experiences of immersive multimedia applications for end users. To prevent the complex computations on 3D data applied by model-based methods, projection-based models have been developed to estimate the QoE by analysing 2D projection views of the point cloud. In this paper, we propose a novel projection-based No-Reference (NR) PCQA method, called Multi-View Adaptive Weighting Point Cloud Quality Assessment (MVAW-PCQA), to predict the QoE of distorted point clouds using six 2D projection views as the input of a convolutional neural network (CNN) architecture. First, multi-view involves independently extracting features from multiple projection views of a point cloud, guaranteeing view-specific features are learned without prematurely mixing spatial information, and preserving the unique contributions of each projection view to the final quality prediction. Then, an adaptive weighting fusion mechanism combines the features extracted from the different projection views by learning their relative importance. This design enables the model to focus on the most informative projections for predicting the point cloud quality. The experimental results demonstrate that our method outperforms state-of-the-art NR-PCQA methods on the SJTU-PCQA dataset in terms of root mean square error (RMSE) and correlation coefficients (Pearson, Spearman, and Kendall), while adopting a lightweight design with a reasonable number of parameters for the trained neural network.

Just Noticeable Difference (JND) thresholds, particularly for quality metrics such as Video Multimethod Assessment Fusion (VMAF), are critical in streaming, helping identify when quality changes become perceptible and reducing redundant bitrate representations. The Satisfied User Ratio (SUR) complements JND by quantifying the percentage of users who do not perceive a difference, offering practical guidance for perceptually optimized streaming. This paper proposes a novel two-branch deep neural network (DNN) for predicting the 75% SUR for VMAF, the encoding level where 75% of viewers cannot perceive degradation. The framework combines handcrafted features (e.g., spatial and temporal indicators such as SI, TI, etc.) and deep learning-based (DL-based) representations extracted via a convolutional neural network (CNN) backbone. The DL-based branch employs a spatio-temporal attention mechanism and a Long Short-Term Memory (LSTM) to capture temporal dynamics, while the handcrafted branch encodes interpretable indicators through a fully connected layer. Both outputs are fused and passed through a lightweight Multilayer Perceptron (MLP) to predict 75% SUR. To improve robustness to noise and label uncertainty, the model is trained using the Smooth-L1 loss. Experiments on the VideoSet dataset show our method outperforms SOTA across all metrics, achieving a notably higher R2score (0.46 vs. 0.36), indicating improved prediction reliability and low computational complexity, making it suitable for real-time video streaming.

Telecommunication technologies are important enablers for both digital and ecological transitions. By offering digital alternatives to traditional modes of transportation and communication, they help reduce carbon footprints while improving access to fundamental services. Particularly in rural and remote areas, telecommunications facilitate access to education, healthcare, and employment, helping to bridge the digital divide. Additionally, telecommunications can promote sustainability by supporting renewable energy usage, gender equality, and circular economies. However, defining the role of telecommunications in sustainability remains complex due to the historical focus on performance rather than long-term societal goals. Given the significance of this theme, this paper aims to provide the reader with a deeper look at the concept of sustainability within the telecommunications sector by examining relevant initiatives and projects. It reviews the major approaches for measuring sustainability and outlines practical approaches for implementing these assessments. Furthermore, the paper explores the proposed network architectures that incorporate Key Value Indicators and discusses major technologies in this area, such as Network Digital Twins and Intent-Based Networking. Through this analysis, the paper aims to contribute to creating sustainable telecommunication networks and broader industries.

Video streaming has dominated Internet traffic, pushing network providers to ensure high-quality services to avoid customer churn. However, predicting streaming quality is challenging due to traffic encryption, requiring extensive network monitoring. While several prediction approaches have been studied, they often overlook resource and energy demands. To address this, we analyze existing methods, quantifying monitoring efficiency to predict video quality degradation. Finally, we highlight significant differences in efficiency, driven by data requirements and the prediction approach, offering insights for providers to select a suitable method for their needs.

The evolution of Next-Generation Networks is driven not only by technological advancements but also by the need to address broader social and environmental concerns. Indeed, in the transition to increasingly interconnected and digitalized societies, it becomes imperative to consider additional key values beyond mere technological performance. Sustainability, encompassing social, economic, and environmental dimensions, emerges as a crucial guiding principle shaping the development and deployment of future networks. This paper explores the evolving landscape of programmable networks, emphasizing the integration of sustainability principles to foster resilient, inclusive, and environmentally conscious network infrastructures. Sustainability principles are used to define Key Value Indicators (KVIs), which can be embedded into an Intent-Based Network to define the user and/or enterprise sustainable goals expressed in terms of intents. Two examples of KVIs are also discussed to analyze the process of reaching their definition.

The increasing demand for quality from multimedia service users is very often addressed by adding more resources (bandwidth and processing power). However, not always does this approach bring an improvement in the perceived quality, whereas it frequently implies an increase in energy consumption (and subsequent higher greenhouse gas emissions). Accordingly, in this paper, we propose a solution to dynamically allocate resources in a 5G heterogeneous network scenario, which aims to identify a trade-off between the overall QoE perceived by the users served by the network when consuming video content and the overall network energy consumption. We considered three types of devices (TV, laptop, and smartphone) for which appropriate QoE and energy consumption models are defined. Extensive simulations have been performed by assigning different levels of importance to QoE and energy. The achieved results show that the network energy consumption can be more than halved by keeping satisfactory QoE. This is particularly true for smartphone users, whereas TV and laptop users have the freedom to choose based on their sensitivity towards sustainability.

Multi-view (MV) learning is a machine learning technique for improving generalization efficiency by learning from different feature subsets derived from multiple sources. We believe this approach can help in Quality of Experience (QoE) modelling by integrating knowledge from different datasets generated by subjective tests conducted for the same or similar applications considering different QoE Influence Factors (IFs). To investigate this subject, in this paper, we present the experiments conducted starting from a complete dataset related to Web browsing sessions that has been artificially divided into two distinct subsets (views). The proposed MV learning approach implements a data fusion technique to integrate extracted features from different views into a unified feature space. To achieve a complete experiment on the entire problem space, all possible combinations of IFs (features) in two distinct partial views (PVs) are considered and trained in the MV approach; the full view (FV) approach, which utilizes the complete dataset, is also considered for performance comparison. Experimental results show the QoE estimation performance achieved by the MV (0.69) is comparable with that of the FV (0.72), although the 2 single views were used for training in the MV case. Moreover, the performance enhancement achieved by the MV compared with the PV is most noticeable when a lower number of features is used to train the models.

This paper proposes NetRate, an Android system monitoring application that can measure and collect network and energy information from Android devices and enables end users to rate the Quality of Experience (QoE) of selected applications. Moreover, NetRate includes a VPN service to limit the network capabilities of the end device to simulate constrained network conditions. NetRate supports the conduction of QoE research studies in the laboratory and the field by collecting different kinds of data from the end device, assisting in setting test conditions (e.g., with the VPN), and collecting subjective feedback.

The last years have been characterized by strong market exploitation of the Internet of Things (IoT) technologies in different application domains, such as Industry 4.0, smart cities, and eHealth. All the relevant solutions should properly address the security issues to ensure that sensor data and actuators are not under the control of malicious entities. Additionally, many applications should at the same time provide low-latency communications, as in the case for instance of remote control of industrial robots. Low latency and security are two of the most important challenges to be addressed for the successful deployment of IoT applications. These issues have been analyzed by several scientific papers and surveys that appeared in the last decade. However, few of them consider the two challenges jointly. Moreover, the security aspects are primarily investigated only in specific application domains or protocol levels and the latency issues are typically investigated only at low layers (e.g., physical, access). This paper addresses this shortcoming and provides a systematic review of state-of-the-art solutions for providing fast and secure IoT communications. Although the two requirements may appear to be in contrast to each other, we investigate possible integrated solutions that minimize device connection and service provisioning. We follow an approach where the proposals are reviewed by grouping them based on the reference architectural layer, i.e., access, network, and application layers. We also review the works that propose promising solutions that rely on the exploitation of the QUIC protocol at the higher levels of the protocol stack.

The utilization of user’s facial- and speech-related features for the estimation of the Quality of Experience (QoE) of multimedia services is still underinvestigated despite its potential. Currently, only the use of either facial or speech features individually has been proposed, and relevant limited experiments have been performed. To advance in this respect, in this study, we focused on WebRTC-based videoconferencing, where it is often possible to capture both the facial expressions and vocal speech characteristics of the users. First, we performed thorough statistical analysis to identify the most significant facial- and speech-related features for QoE estimation, which we extracted from the participants’ audio-video data collected during a subjective assessment. Second, we trained individual QoE estimation machine learning-based models on the separated facial and speech datasets. Finally, we employed data fusion techniques to combine the facial and speech datasets into a single dataset to enhance the QoE estimation performance due to the integrated knowledge provided by the fusion of facial and speech features. The obtained results demonstrate that the data fusion technique based on the Improved Centered Kernel Alignment (ICKA) allows for reaching a mean QoE estimation accuracy of 0.93, whereas the values of 0.78 and 0.86 are reached when using only facial or speech features, respectively.

In this article, we propose a Hand Gesture Recognition (HGR) system based on a novel deep transformer (DT) neural network for media player control. The extracted hand skeleton features are processed by separate transformers for each finger in isolation to better identify the finger characteristics to drive the following classification. The achieved HGR accuracy (0.853) outperforms state-of-the-art HGR approaches when tested on the popular NVIDIA dataset. Moreover, we conducted a subjective assessment involving 30 people to evaluate the Quality of Experience (QoE) provided by the proposed DT-HGR for controlling a media player application compared with two traditional input devices, i.e., mouse and keyboard. The assessment participants were asked to evaluate objective (accuracy) and subjective (physical fatigue, usability, pragmatic quality, and hedonic quality) measurements. We found that (i) the accuracy of DT-HGR is very high (91.67%), only slightly lower than that of traditional alternative interaction modalities; and that (ii) the perceived quality for DT-HGR in terms of satisfaction, comfort, and interactivity is very high, with an average Mean Opinion Score (MOS) value as high as 4.4, whereas the alternative approaches did not reach 3.8, which encourages a more pervasive adoption of the natural gesture interaction.

The increasing use of ICT has raised concerns about its negative impact on energy consumption and$CO_{2}$emissions. To address this issue, there is a need to better understand the trade-off between Quality of Experience (QoE) and sustainable video streaming services. In this study, we designed and conducted a subjective assessment to investigate the impact of video resolution, different types of luminance, and different end devices on the QoE and energy consumption of video streaming services. Then, we applied statistical models (Analysis of Variance and t-test) to subjective data to find out what factors influence the QoE the most and consume more energy. The obtained results suggest that under specific conditions (e.g., dark or bright ambient, low device backlight luminance, small-screen device) the users could be encouraged towards a trade-off between acceptable QoE and sustainable (green) choices because spending more energy (e.g., streaming higher-quality video) would not provide noticeable QoE enhancement.

In the past decade, several algorithms have been proposed to monitor people's mobility based on the analysis of management messages generated by Wi-Fi devices and which rely on the factory physical addresses to identify the source. However, since 2012, major mobile device manufacturers have started protecting their clients' privacy through non-reversible encryption of these identifiers and the omission of other infor-mation. To still protect user privacy and at the same time allow for the identification of frames generated by the same source, we have conducted an extensive analysis of the major fields of these messages, which are called Information Elements. To this, we have analysed an open dataset of Probe Requests sent by individual devices that were captured in isolated or pseudo-isolated environments. In the first part of our analysis, we used the Random Forest algorithm to evaluate the importance of Information Elements for the clustering of Probe Requests, and we discovered that three of them are more valuable than the others. By exploiting this outcome, we implemented a clustering algorithm and found the best settings which allowed us to achieve the correct Probe Requests clustering on average in 92% of cases.

WebRTC-based applications allow for real-time communications that are subject to network impairments affecting the end user's Quality of Experience (QoE). In this paper, we conducted subjective tests involving 20 people to investigate the conversational quality of a two-party WebRTC-based audiovisual telemeeting service. A dedicated system was implemented to introduce controlled network impairments (delay, jitter, and packet loss) to impair the communication between the parties. In addition, test participants had to rate the perceived QoE for the audio, the video, and the overall service, as well as the three emotional dimensions, i.e., valence, arousal, and dominance. Extensive results were obtained regarding the impact of the network impairments on the multimedia quality, the emotional dimensions, and the communication feasibility.

The Metaverse provides a novel experience to the user, by opening the doors to social-based multiuser environments merging physical reality with digital virtuality. In this paper, we present an initial analysis of the Quality of Experience (QoE) in the Metaverse. We first consider traditional influence factors (human, system, and context). Then, we introduce the social and economic dimensions of the Metaverse as additional factors to be considered for QoE assessment. Finally, we discuss what QoE assessment methods can be more suitable for Metaverse applications, with a particular focus on implicit assessment methods (e.g., physiological, human cognitive, affective behaviour).

Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to thebestdevices, according to the context and task requirements. The main purpose of this article is to study the different connotations of the concept of TAefficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. This article first analyzes the domains of connected devices where TA has an important role, which brings to this classification: 1) Internet of Things (IoT); 2) sensor and actuator networks (SANs); 3) multirobot systems (MRSs); 4) mobile crowdsensing (MCS); and 5) unmanned aerial vehicles (UAV). This article then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-of-the-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. This article also discusses the issues that need to be addressed by future research activities, i.e., allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; the list of tasks performed by objects; and designing TA strategies where network service providers have a role in TA functionalities’ provisioning.

The development of future wireless networks focuses on providing services with strict, dynamic, and diverse quality of service (QoS) requirements. In this sense, the network slicing paradigm arises as a critical piece on the efficient allocation and management of network resources, allowing for dividing the network into several logical networks with specific functionalities and performance. This paper aims at finding the best combination of access network and network slices over a heterogeneous environment to fulfill users’ requests and optimize network resources usage. We propose the Dynamic radio Access selection and Slice Allocation (DASA) algorithm, flexibly adapted to network conditions, user priorities, and mobility behavior. DASA is based on a multi-attribute decision making (MADM) and analytical hierarchy process (AHP) to face the complex problem of network selection. Moreover, it uses a cooperative game theory approach to handle load balancing during overload situations. This work presents an integral solution that combines software-defined network (SDN) and network function virtualization (NFV) technologies to improve network performance and user satisfaction. DASA algorithm is evaluated through network-level simulations, focusing on flexibility and the effective utilization of network resources during network selection and load balancing mechanisms.

With the recent advances of the Internet of Things (IoT), and the increasing accessibility to ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as social relationships explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as artificial social intelligence (ASI) that has the potential to tackle the social relationships explosion problem. This article discusses the role of IoT in social relationships management, the problem of social relationships explosion in IoT, and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.

Social Internet of Things comes as a new paradigm of Internet of Things to solve the problems of network discovery, navigability, and service composition. It aims to socialize the IoT devices and shape the interconnection between them into social interaction just like human beings. In IoT scenarios, a device can offer multiple services and different devices can offer the same services with different parameters and interest factors. The proliferation of offered services led to difficulties during service filtering and customization, this problem is known as services explosion. The selection of a suitable service that fits the requirements of the applications and devices is a challenging task. Several works have addressed service discovery, composition, and selection in IoT. However, these works did not emphasize on the fact that incorporating the users’ social features can increase the efficiency of the recommended services and help us to offer context-aware services. In this article, we present a service recommendation system that takes advantage of the social relationships between devices’ owners, where the recommendation is based on the different relationships between the service requester and service provider. Experimental results show, in the context of IoT, that incorporating the users’ social relationships in service recommendation increases the accuracy and diversity of the offered services.

In this paper, we present an open source Software-Defined Networking (SDN) based emulation platform called Timber. We aim to provide the research community with an experimental tool for the design and evaluation of the new Quality of Experience (QoE) management and monitoring procedures for video streaming. To this aim, the main functionalities of Timber include: i) an SDN application for taking QoE-aware management decisions; ii) an SDN controller to monitor the network's QoS (Quality of Service) and implement network management actions, such as network slicing and Multiprotocol Label Switching (MPLS) based prioritization operations; iii) a complete video streaming application including a multimedia server and a DASH-based client video player; iv) a user-end probe at the client video player to monitor QoE-related video application parameters, which are stored in a database that can be accessed by the SDN application; v) data analysis tools, which enable easy data visualization of measured QoS and QoE metrics as well as execution of statistical analysis of experimental results. In this article, we introduce and describe the main characteristics and functionalities of Timber as well as the implementation details. Finally, we provide experimental results of a video streaming scenario to demonstrate the capability of Timber to implement and test QoE-aware management approaches.

The Quality of Experience (QoE) management procedures for multimedia services benefit from an effective collaboration between the Mobile Network Operators (MNOs) and the Over-The-Top (OTT) service providers as the former can allocate the appropriate network resources to the users and the latter has access to key influence factors for having a proper view of the provided QoE. One successful collaboration model is the zero-rated data rate approach, according to which the MNO limits the data rate of the users towards the collaborating OTT applications with the benefit for the user that the generated traffic is not counted in her monthly contract data limit. Accordingly, the MNO may reduce the network congestion, and the users are encouraged to select the collaborating OTT applications. Though, this approach does not consider the resulting QoE, which may vary significantly from one user to another even if the same throughput is given. Based on this consideration, in this paper, we proposed the zero-rated QoE approach, according to which the limit is introduced in terms of QoE rather than throughput. This clearly requires a stronger collaboration between the OTT and the MNO, as the first has to give access to the second to quality influence factors and the latter to allocate resources according to the predicted QoE. The contributions of this paper are: the introduction of the novel zero-rated QoE approach with particular reference to video streaming services; the definition of novel components in the 3GPP architecture so that this approach can be introduced; the definition of the algorithm for the allocation of the appropriate radio resources to each user; a simulation analysis where the proposed approach is compared with respect to the former zero-rated approach, which shows significant improvements in terms of average provided quality and quality fairness at the same overall throughput.

This article investigates the possibility to estimate the perceived Quality of Experience (QoE) automatically and unobtrusively by analyzing the face of the consumer of video streaming services, from which facial expression and gaze direction are extracted. If effective, this would be a valuable tool for the monitoring of personal QoE during video streaming services without asking the user to provide feedback, with great advantages for service management. Additionally, this would eliminate the bias of subjective tests and would avoid bothering the viewers with questions to collect opinions and feedback. The performed analysis relies on two different experiments: i) a crowdsourcing test, where the videos are subject to impairments caused by long initial delays and re-buffering events; ii) a laboratory test, where the videos are affected by blurring effects. The facial Action Units (AU) that represent the contractions of specific facial muscles together with the position of the eyes' pupils are extracted to identify the correlation between perceived quality and facial expressions. An SVM with a quadratic kernel and a k-NN classifier have been tested to predict the QoE from these features. These have also been combined with measured application-level parameters to improve the quality prediction. From the performed experiments, it results that the best performance is obtained with the k-NN classifier by combining all the described features and after training it with both the datasets, with a prediction accuracy as high as 93.9% outperforming the state of the art achievements.

In this work, we propose a remuneration-aided Game theoretical solution for task allocation in cognitive radio (CR) enabled Internet of things (IoT) scenarios, where cognitive nodes (CNs) in close proximity and with similar sensing capabilities are clustered around a cluster head (CH). We consider a framework in which task allocation in the system is driven by CNs with spectrum sensing capabilities. In the proposed approach, the CH assigns a remuneration to CNs for their contribution in spectrum sensing prior to initiating the task allocation procedure. Such remunerations can be used by CNs in proposing the bids to win the task in the Game. Hence a non-cooperative Game approach modelled as an auction process is proposed. We show that the proposed framework is able to exploit cognitive behaviour efficiently in conditions suitable for cognitive radios (low spectrum occupancy), and under the same conditions the overall system utility increases by 29% w.r.t the case when licensed users (LUs) occupy the band 70% of the time. Additionally, the framework allows the system to reap benefits of energy efficiency while experimenting cognitivity.

The physical context, i.e., the characteristics of location and space where the multimedia service is consumed, may strongly influence the overall perceived Quality of Experience (QoE). In this paper, we investigate the effects of ambient illumination and noise on two multimedia consumption scenarios: watching a video on TV and reading a comic strip on tablet. To this aim, we organized an experiment considering different combinations of ambient illumination and introducing a disturbing noise. Then, we conducted a subjective quality assessment involving 20 people, who were asked to rate the perceived QoE using the 5-level Absolute Category Rating (ACR) quality scale and to express their emotions completing the Self-Assessment Manikin (SAM) questionnaire. The impact of illumination and noise on ACR ratings and SAM scores is evaluated computing the Multivariate Analysis of Variance (MANOVA). Finally, a QoE prediction model based on illumination and noise context factors is presented.

As known from everyday contexts of multimedia usage, suddenly occurring quality impairments are capable of causing strong negative emotions in human users. This is particularly the case if the displayed content is highly relevant to current motives and behavioral goals. The present study investigated the effects of visual degradations on quality perception and emotional state of participants who were exposed to a series of short video clips. After each video playback, participants had to decide whether a certain event happened in the video. For data collection, subjective measures of quality and emotion were complemented by behavioral measures derived from capturing participants' spontaneous facial expressions. For data analysis, two general approaches were combined: First, a multivariate analysis of variance approach allowed to examine the effects of visual degradation factors on perceived quality and subjective emotional dimensions. It mainly revealed that perceived quality and emotional valence were both sensitive to degradation intensity, whereas the impact of degradation length was limited when task-relevant video content had already been obscured. Second, using a machine learning approach, an automatic Video Quality of Experience (VQoE) prediction system based on the recorded facial expressions was derived, demonstrating a strong correlation between facial expressions and perceived quality. Hereby, estimates of VQoE might be delivered in an objective, continuous and concealed manner, thus diminishing any further need for subjective self-reports.

Recent studies implemented collaboration between Over-The-Top (OTT) service and Internet Service Provider (ISP) concerning information exchange between the providers. This information is used to accurately predict the delivered Quality of Experience (QoE) and decide the correcting network management actions. In this scenario, we aim to investigate the impact of the frequency of information exchange between OTT and ISP on end users' QoE and network resource utilization. Firstly, we propose our information-centric QoE-aware collaborative service management approach by defining the type of information to be acquired and exchanged between OTT and ISP. Secondly, we present our platform based on the Software-Defined Networking (SDN) paradigm, which we used to conduct the experiments. Finally, we conduct experiment results that compare the proposed collaborative approach with the case of no collaboration when the sampling interval of information exchange varies between 2 s and 32 s. The experiment results show that a higher frequency of information exchange may result in better network reliability and delivered QoE, but a frequency higher than 1/4 Hz may not further improve the delivered QoE.

In recent years identifier/locator splitting (ILS) has been proposed as a promising future Internet solution to the so-called “semantic overload of addresses” problem. It is currently under debate whether the ILS approach, which entails identifier-to-locator resolution procedures, could ensure scalability, session continuity, and mobility across heterogeneous multiaccess networks as demanded by Internet of Things (IoT). In this paper, it is proposed to exploit the Social IoT concept to address the above issues. More specifically, a scheme is introduced that browses the social graph of devices to find information about the locator of the intended destination. Results show that the proposed solution outperforms the alternative ILS approaches in terms of number of hops and latency incurred to accomplish the resolution procedure, at the cost of a slight increase in the storage demands to track social relationships.

The Social Internet of Things (SIoT) is a novel communication paradigm according to which the objects connected to the Internet create a dynamic social network that is mostly used to: route information and service requests, disseminate data, and evaluate the trust level of each member of the network. In this paper, the SIoT paradigm is applied to a scenario where geolocated sensing tasks are assigned to fixed and mobile devices, providing the following major contributions. The SIoT model is adopted to find the objects that can contribute to the IoT application by crawling the social network through the nodes profile and trust level. A new algorithm to address the resource management issue is proposed so that sensing tasks are fairly assigned to the objects in the SIoT. To this, an energy consumption profile is created per device and task, and shared among nodes of the same category through the SIoT. The resulting solution is also implemented in the SIoT-based Lysis platform. Emulations have been performed, which showed an extension of the time needed to completely deplete the battery of the first device of more than 40% with respect to alternative approaches.

In this study we consider the scenario of sensors belonging to different platforms and owned by different owners that join the efforts in an opportunistic way to improve the overall sensing capabilities in a given geographical area by forming clusters of nodes. The considered nodes have cognitive radio and exploit device-to-device communications. A solution is proposed which relies on a Cluster Head (CH) that guides the whole task allocation strategy. The addressed challenges are the following: i) collaborative spectrum sensing for effective communications within the cluster; ii) assignment of each request of sensing tasks to a single node in the cluster. The first challenge is addressed by proposing a collaborative sensing procedure where each node communicates to the CH the received signal energy of licensed users so that the latter makes a decision on the availability of the band by fusing the received information towards a minimisation of the uncertainty in detecting the free spectrum. The second challenge is addressed by proposing a non-cooperative Game theory based approach in which cluster nodes make effort to selfishly increase utility by winning the task. Each node takes part to the competition by considering two elements: the gain that is won for its contribution to sensing and for the execution of the task (in case it wins the competition); the cost in terms of energy to be consumed in case the task is executed. A Nash Equilibrium Point (NEP) is found for the aforementioned game in which each object has no incentive to deviate uni-laterally from the NEP. Extensive simulations are performed to evaluate the impact of probability of false alarm, utility function weighting factors and presence of licensed users on the cumulative system utility.

The Identifier/Locator Splitting (ILS) paradigm has been proposed in the future Internet research arena to address the semantic overload of IP addresses. In this paper the integration of the Social Internet of Things (SIoT) concept into ILS solutions is investigated. Indeed, SIoT has been recently argued as a promising approach to improve the performance of identifier-to-locator mapping procedures. More specifically, we describe the general approach, propose an architecture and show some preliminary performance results obtained through simulations. The proposed SIoT-enabled ILS architecture relies on distributed repositories of virtual counterparts of entities that store Friendship Tables specifically introduced to enable the mapping information to be retrieved by surfing the social graph. Achieved results in a smart campus scenario show that the proposed approach achieves better performance, in terms of number of hops, than a mapping solution based on Distributed Hash Tables (DHT).

The new generation of LTE (Long Term Evolution) networks provides ubiquitous broadband access to mobile devices matching land communications in quality and speed. However, to optimize network resource usage in a dynamic environment network operators need models and strategies to constantly assess and manage the end-user's Quality of Experience (QoE). Given the importance of these activities, in the current paper, we focus on quality monitoring and the usage of QoE-agents in an LTE-Advanced Pro network. Specifically, we identify the location and the operation of the QoE-Agents based on the accuracy of the measurements and the load in the network considering the frequency of the measurements and the running applications. Emulations have been also carried out to evaluate two scenarios with different network conditions and we made experiments with different quality sampling rates and different application configurations. The preliminary results have shown that the proposed strategy brings to acceptable errors from our measurements, low CPU utilization and acceptable memory utilization.

Node, service and information discovery as well as trust management are key issues that characterize the IoT when huge numbers of nodes have to collaborate to support the deployed applications. A recent promising proposal, with the ability to address these issues, is the Social IoT (SIoT) paradigm, whose main principle is to enable objects to autonomously establish social links with to each other (adhering to rules set by their owners). To be able to test and validate this ability, significant datasets regarding objects' networks (node description, typology, activities, exchanged traffic) are needed, which however are not completely available. This paper addresses this issue by presenting a dataset that has been realized on the basis of real IoT objects available in the city of Santander and categorized following the typologies and data model for objects introduced in the FIWARE Data Models. Object profiles and guidelines for the relationships' creation for the SIoT are described and the obtained data and the resulting social network is made available to the research community.

In this paper, we present an open source Software-Defined Networking (SDN) based emulation platform called Timber. It is aimed at providing the research community with a tool for experimenting new Quality of Experience (QoE) management procedures and tools in multimedia service delivery. Timber is developed on the top of Mininet SDN emulator and Ryu SDN controller, which provides the major functionalities of the traffic engineering abstractions in SDN environment. Moreover, the platform provides an actual complete video streaming application including the implementation of the server side and client side probes for QoE measurements which have functionalities to store the quality measurements into the cloud database accessible to the SDN controller application. In this paper, we first discuss the general architecture and framework of Timber. Secondly, we provide the implementation details and major functionalities of the platform. Thirdly, we provide experimental results to highlight the major functionalities of Timber by 4 different scenarios which include traffic shaping through DiffServ and dynamic resource allocation by queuing strategies.

This paper investigates the applicability of QoE management on Smart City services. First, we analyze how quality management is implemented for traditional public services (services for which the presence of ICT technologies is limited or not necessary). We then propose a potential framework for the QoE management in Smart City services (traditional public services supported by an important, but not essential, presence of ICT systems), which is based on ICT systems for QoE prediction and management. Finally, we highlight the challenges to be addressed in the near future.

In the last decade, we have been experiencing an increasing level of intelligence that the objects in the Internet of Things (IoT) have been augmented with, especially in the direction of giving them cognitive and socialization capabilities. We believe that this evolution should go further in the direction of the Emotional Intelligence, which allows humans to be successful in their lives. EmIoT is the resulting paradigm that we propose, which is aimed to increase the Quality of Experience delivered by IoT applications by making IoT capable of: understanding peoples needs by observing them; better management of its own resources on the basis of users emotional state; creating a level of affection to be used for leveraging the level of interaction between IoT and users. The paper's contribution lies in the definition of the paradigm, the analysis of the new functionalities the IoT should be augmented with, and the preliminary investigation of the possible EmIoT architecture.

It is a matter of fact that quality of experience (QoE) has become one of the key factors determining whether a new multimedia service will be successfully accepted by the final users. Accordingly, several QoE models have been developed with the aim of capturing the perception of the user by considering as many influencing factors as possible. However, when it comes to adopting these models in the management of the services and networks, it frequently happens that no single provider has access to all of the tools to either measure all influencing factors parameters or control over the delivered quality. In particular, it often happens to the over-the-top (OTT) and Internet service providers (ISPs), which act with complementary roles in the service delivery over the Internet. On the basis of this consideration, in this article we first highlight the importance of a possible OTT-ISP collaboration for a joint service management in terms of technical and economic aspects. Then we propose a general reference architecture for a possible collaboration and information exchange among them. Finally, we define three different approaches, namely joint venture, customer lifetime value based, and QoE fairness based. The first aims to maximize the revenue by providing better QoE to customers paying more. The second aims to maximize the profit by providing better QoE to the most profitable customers (MPCs). The third aims to maximize QoE fairness among all customers. Finally, we conduct simulations to compare the three approaches in terms of QoE provided to the users, profit generated for the providers, and QoE fairness.

In this paper, we illustrate a Software Defined Network (SDN)-based architecture for Quality of Experience (QoE) management that solves two of the major problems of current networking technologies which are related to the limitations in scalability and flexibility. Its advantage is the exploitation of the virtualization features of the network nodes and devices to flexibly deploy monitoring and control functions in the different points of the network according to the SDN control functions. As a result the QoE monitoring and management is deployed at the application layer on top of the controller. In order to evaluate the proposed framework and architecture, a platform has been developed, which is called QoE-MoMa (QoE-Monitoring and Management) platform, making use of the Opendaylight solution and Mininet emulation environment. To evaluate QoE-MoMa, we focused on the video streaming service, whose final quality has been evaluated using the estimated MOS (eMOS) model that mostly considers rebuffering events, duration of the rebuffering, switch quality rates, video resolution, and quantization parameter. The results show the efficiency of the proposed approach observing that higher QoE level is achieved if we consider application and network parameters. In conclusion, we consider that QoE-MoMa is useful as a QoE monitoring and management tool for a variety of services and can be deployed on a real network conveniently.

This paper focuses on passive Quality of Experience (QoE) monitoring at user end devices as a necessary activity of the ISP (Internet Service Provider) for an effective quality-based service delivery. The contribution of the work is threefold. Firstly, we highlight the opportunities and challenges for the QoE monitoring of the Over-The-Top (OTT) applications while investigating the available interfaces for monitoring the deployed applications at the end-device. Secondly, we propose a multilayer passive QoE monitor for OTT applications at the user terminal with ISPs prospect. Five layers are considered: user profile, context, resource, application and network layers. Thirdly, we consider YouTube as a case study for OTT video streaming applications in our experiments for analyzing the impact of the monitoring cycle on the user end device resources, such as the battery, RAM and CPU utilization at end user device.

In this paper the MIFaaS (Mobile-IoT-Federation-as-a-Service) paradigm is proposed to support delay sensitive applications in the Internet of Things (IoT). This objective is reached by leveraging on the federation of distributed services and things at the infrastructure Edge and exploiting the real-world awareness and capabilities of IoT devices at the ground. MIFaaS enables value-added services by implementing the dynamic cooperation among private/public clouds of IoT objects with the purpose to enhance the efficiency in the provisioning of delay-constrained IoT services and increase the number of successfully delivered IoT services. The proposed paradigm is studied in a cellular environment based on standard Long Term Evolution (LTE). The simulative results we present demonstrate how the proposed federation solution of private/public IoT clouds outperforms alternative solutions with no federations and support of resources offered by the Cloud. Moreover, an analysis of the limitations and of the possible enhancements for cellular systems to support the proposed paradigm is drawn.

Future networks will be accompanied by new heterogeneous requirements in terms of end-users Quality of Experience (QoE) due to the increasing number of application scenarios being deployed. Network softwarization technologies such as Software Defined Networks (SDNs) and Network Function Virtualization (NFV) promise to provide these capabilities. In this paper, a novel QoE-driven resource allocation mechanism is proposed to dynamically assign tasks to virtual network nodes in order to achieve an optimized end-to-end quality. The aim is to find the best combination of network node functions that can provide an optimized level of QoE to the end users though node cooperation. The service in question is divided in tasks and the neighbor nodes negotiate the assignment of these considering the final quality. In the paper we specifically focus on the video streaming service. We also show that the agility provided by SDN/NFV is a key factor for enhancing video quality, resource allocation and QoE management in future networks. Preliminary results based on the Mininet network emulator and the OpenDaylight controller have shown that our approach can significantly improve the quality of a transmitted video by selecting the best path with normalized QoS values.

In this work, we propose a QoE-aware collaboration approach between Over-The-Top providers (OTT) and Internet Service Providers (ISP) based on the maximization of the profit by considering the user churn of Most Profitable Customers (MPCs), which are classified in terms of the Customer Lifetime Value (CLV). The contribution of this work is multifold. Firstly, we investigate the different perspectives of ISPs and OTTs regarding QoE management and why they should collaborate. Secondly, we investigate the current ongoing collaboration scenarios in the multimedia industry. Thirdly, we propose the QoE-aware collaboration framework based on the CLV, which includes the interfaces for information sharing between OTTs and ISPs and the use of Content Delivery Networks (CDN) and surrogate servers. Finally, we provide simulation results aiming at demonstrating the higher profit is achieved when collaboration is introduced, by engaging more MPCs with respect to current solutions.

This paper presents Lysis, which is a cloud-based platform for the deployment of Internet of Things (IoT) applications. The major features that have been followed in its design are the following: each object is an autonomous social agent; the platform as a service (PaaS) model is fully exploited; reusability at different layers is considered; the data is under control of the users. The first feature has been introduced by adopting the social IoT concept, according to which objects are capable of establishing social relationships in an autonomous way with respect to their owners with the benefits of improving the network scalability and information discovery efficiency. The major components of PaaS services are used for an easy management and development of applications by both users and programmers. The reusability allows the programmers to generate templates of objects and services available to the whole Lysis community. The data generated by the devices is stored at the object owners cloud spaces. This paper also presents a use-case that illustrates the implementation choices and the use of the Lysis features.

The ever-increasing network traffic and user expectations at reduced cost make the delivery of high Quality of Experience (QoE) for multimedia services more vital than ever in the eyes of Internet Service Providers (ISPs). Real-time quality monitoring, with a focus on the user, has become essential as the first step in cost-effective provisioning of high quality services. With the recent changes in the perception of user privacy, the rising level of application-layer encryption and the introduction and deployment of virtualized networks, QoE monitoring solutions need to be adapted to the fast changing Internet landscape. In this contribution, we provide an overview of state-of-the-art quality monitoring models and probing technologies, and highlight the major challenges ISPs have to face when they want to ensure high service quality for their customers.

This paper presents an IoT cloud-based state estimation system for distribution networks in which the PMUs (Phasor Measurement Units) are virtualized with respect to the physical devices. In the considered system only application level entities are put in the cloud, whereas virtualized PMUs are running in the communication network edge (i.e. closer to the physical objects) in order to have a certain degree of local logic, which allows to implement a bandwidth-efficient and smart data transmission to the involved applications in the cloud. The major contributions of the paper are the following: we demonstrate that a cloud-based architecture is capable of achieving the QoS level required by the specific state estimation application; we show that implementing a certain local logic for data transmission in the cloud, the result of the state estimation is not degraded with respect to the case of an estimation that takes place frequently at fixed intervals; we show the results in terms of latency and reduced network load for a reference smart grid network.

Fifth generation (5G) systems are expected to introduce a revolution in the ICT domain with innovative networking features, such as device-to-device (D2D) communications. Accordingly, in-proximity devices directly communicate with each other, thus avoiding routing the data across the network infrastructure. This innovative technology is deemed to be also of high relevance to support effective heterogeneous objects interconnection within future IoT ecosystems. However, several open challenges shall be solved to achieve a seamless and reliable deployment of proximity-based communications. In this paper, we give a contribution to trust and security enhancements for opportunistic hop-by-hop forwarding schemes that rely on cellular D2D communications. To tackle the presence of malicious nodes in the network, reliability and reputation notions are introduced to model the level of trust among involved devices. To this aim, social-awareness of devices is accounted for, to better support D2D-based multihop content uploading. Our simulative results in small-scale IoT environments, demonstrate that data loss due to malicious nodes can be drastically reduced and gains in uploading time be reached with the proposed solution.

The objective of this work is the investigation of a possible collaboration between Over-The-Top (OTTs) service providers and Internet Service Providers (ISPs), which is centered around the Quality of Experience (QoE). Initially, we define a reference architecture with the required modules and interfaces for the interaction between the two providers. Then, we focus on the modeling of the revenue, whose maximization drives the collaboration. It is considered as depending on the user churn, which in turn is affected by the QoE and is modeled using the Sigmoid function. We illustrate simulation results based on our proposed collaboration approach which highlights how the proposed strategy increases the revenue generation and QoE for both players hence providing a ground for ISP to join the loop of revenue generation between OTT and users.

In this paper, a Quality of Experience (QoE)-aware Smart Home Energy Management (SHEM) system is proposed. Firstly, a survey has been conducted on 64 people to investigate the degree of satisfaction perceived when the starting time of appliances was postponed or anticipated with respect to the preferred time. Secondly, the results were clustered in different profiles using the k-means algorithm to control appliances' working time according to the detected user profile. Thirdly, a SHEM system is run that relies on two algorithms: the QoE-aware Cost Saving Appliance Scheduling (Q-CSAS) and the QoE-aware Renewable Source Power Allocation (Q-RSPA). The former is aimed at scheduling controllable loads based on users' profile preferences and Time-of-Use (TOU) electricity prices, thus taking into account the level of annoyance perceived when a task is postponed or anticipated. The latter re-allocates the starting time of appliances whenever a surplus of energy has been made available by Renewable Energy Sources (RES). This re-allocation takes place using a distributed max-consensus negotiation algorithm. The objective is that of scheduling the appliances starting time so that a trade-off between cost saving and annoyance perceived is achieved. As demonstrated by simulation results, the two algorithms ensure a cost saving that goes from 19% to 84% depending on the presence of RES, with a resulting average annoyance factor value of 1.01 to 1.03.

Summary form only given. All market and technology studies forecast an explosive growth in the number of "things" that will be connected to the Internet. The resulting network is what is commonly known as the "Internet of Things" (IoT). When compared to the traditional Internet, the extremely high complexity of the IoT environments (usually characterized by a huge number of nodes, high heterogeneity of their resources and capabilities, uncertainty on their trustworthiness, etc.) poses new challenges that cannot be faced by even very smart objects singularly.Social behavior is the answer found by several creatures to face the complexity of the surrounding environment. Accordingly, the concept of Social Internet of Things (SIoT) has been recently introduced and is the subject of a rapidly increasing research effort.During the tutorial the introduction of social notions into the IoT will be motivated, the basic concepts of the SIoT paradigm explained, and the existing related research results and industrial experimentations surveyed. Besides, the architecture of a sample SIoT-based platform will be detailed together with some exemplary applications. Reference web site for this tutorial: http://www.social-iot.org.

HTTP adaptive streaming technology is now widely adopted in multimedia services because of its ability to provide adaptation to the streaming context, especially characteristics of end-user devices and dynamic network conditions. There are various studies targeting the evaluation of the Quality of Experience (QoE) in this framework. However, none has considered the scenario of the user changing the viewing device during the streaming session, which is the objective of this paper. It provides the following major contributions: definition of the multi-device streaming session scenario; the implementation of a realistic testing case; the execution of subjective tests involving 28 people; and the detailed analysis of the influence of the devices' switching events.

Energy usage optimization in Smart Homes is a critical problem: over 30% of the energy consumption of the world resides in the residential sector. Usage awareness and manual appliance control alone are able to reduce consumption by 15%. This result could be improved if appliance control is automatic, especially if renewable sources are present locally. In this paper, a Smart Home Energy Management system that aims at automatically controlling appliances in groups of smart homes belonging to the same neighborhood is proposed. Not only is electric power distribution considered, but also renewable energy sources such as wind micro-turbines and solar panels. The proposed strategy relies on two algorithms. The Cost Saving Task Scheduling algorithm is aimed at scheduling high-power controllable loads during off-peak hours, taking into account the expected usage of the non-controllable appliances such as fridge, oven, etc. This algorithm is run whenever a new need of energy from a controllable load is detected. The Renewable Source Power Allocation algorithm re-allocated the starting time of controllable loads whenever surplus of renewable source power is detected making use of a distributed max-consensus negotiation. Performance evaluation of the algorithms tested proves that the proposed approach provides an energy cost saving that goes between 35% and 65% with reference to the case where no automatic control is used.

The huge number of nodes that is expected to join the Internet of Things in the short term will add major scalability issues to several procedures. A recent promising approach to these issues is based on social networking solutions to allow objects to autonomously establish social relationships. Every object in the resulting Social IoT (SIoT) exchanges data with its friend objects in a distributed manner to avoid the need for centralized solutions to implement major functionalities, such as: node discovery, information search and trustworthiness management. However, the number and types of established friendship affects network navigability. This paper addresses this issue proposing an efficient, distributed and dynamic strategy for the objects to select the right friends for the benefit of the overall network connectivity. The proposed friendship selection model relies on a Shapley-value based algorithm mapping the friendship selection process in the SIoT onto the coalition formation problem in a corresponding cooperative game. The obtained results show that the proposed solution is able to ensure global navigability, measured in terms of average path length among two nodes in the network, by means of a distributed and wise selection of the number of friend objects a node has to handle.

The Internet of Things (IoT) is expected to be overpopulated by a very large number of objects, with intensive interactions, heterogeneous communications, and millions of services. Consequently, scalability issues will arise from the search of the right object that can provide the desired service. A new paradigm known as Social Internet of Things (SIoT) has been introduced and proposes the integration of social networking concepts into the Internet of Things. The underneath idea is that every object can look for the desired service using its friendships, in a distributed manner, with only local information. In the SIoT it is very important to set appropriate rules in the objects to select the right friends as these impact the performance of services developed on top of this social network. In this work, we addressed this issue by analyzing possible strategies for the benefit of overall network navigability. We first propose five heuristics, which are based on local network properties and that are expected to have an impact on the overall network structure. We then perform extensive experiments, which are intended to analyze the performance in terms of giant components, average degree of connections, local clustering, and average path length. Unexpectedly, we discovered that minimizing the local clustering in the network allowed for achieving the best results in terms of average path length. We have conducted further analysis to understand the potential causes, which have been found to be linked to the number of hubs in the network.

The realization of the Internet of Things (IoT) paradigm relies on the implementation of systems of cooperative intelligent objects with key interoperability capabilities. In order for objects to dynamically cooperate to IoT applications' execution, they need to make their resources available in a flexible way. However, available resources such as electrical energy, memory, processing, and object capability to perform a given task, are often limited. Therefore, resource allocation that ensures the fulfilment of network requirements is a critical challenge. In this paper, we propose a distributed optimization protocol based on consensus algorithm, to solve the problem of resource allocation and management in IoT heterogeneous networks. The proposed protocol is robust against links or nodes failures, so it's adaptive in dynamic scenarios where the network topology changes in runtime. We consider an IoT scenario where nodes involved in the same IoT task need to adjust their task frequency and buffer occupancy. We demonstrate that, using the proposed protocol, the network converges to a solution where resources are homogeneously allocated among nodes. Performance evaluation of experiments in simulation mode and in real scenarios show that the algorithm converges with a percentage error of about±5% with respect to the optimal allocation obtainable with a centralized approach.

The integration of social networking concepts into the Internet of things has led to the Social Internet of Things (SIoT) paradigm, according to which objects are capable of establishing social relationships in an autonomous way with respect to their owners with the benefits of improving the network scalability in information/service discovery. Within this scenario, we focus on the problem of understanding how the information provided by members of the social IoT has to be processed so as to build a reliable system on the basis of the behavior of the objects. We define two models for trustworthiness management starting from the solutions proposed for P2P and social networks. In the subjective model each node computes the trustworthiness of its friends on the basis of its own experience and on the opinion of the friends in common with the potential service providers. In the objective model, the information about each node is distributed and stored making use of a distributed hash table structure so that any node can make use of the same information. Simulations show how the proposed models can effectively isolate almost any malicious nodes in the network at the expenses of an increase in the network traffic for feedback exchange.

Internet of Things (IoT) applications will be based on the interactions between smart objects. In many applications such interactions are possible (or meaningful) when objects are close to each others, i.e., there is a co-presence. Unfortunately, to date there are no data traces providing information about the copresence of smart objects. Indeed, several mobility traces reporting humans' movements are available, but none of them contains information about the interactions between their objects. Objective of the work reported in this paper is to fill this gap. To this purpose, we start from user mobility patterns available from several datasets. We associate to each user a set of objects, based on a survey we have carried out over around 450 users. Accordingly, we analyze the statistics about the co-presence of objects. We carry out our analysis by exploiting the tools developed for the analysis of complex networks. Our objective is to identify the objects which are likely to play a key role in the interactions between smart objects in the IoT.

Nodes in Wireless Sensor Networks (WSNs) are becoming more and more complex systems with the capabilities to run distributed structured applications. Which single task should be implemented by each WSN node needs to be decided by the application deployment strategy by taking into account both network lifetime and execution time requirements. In this paper, we propose an adaptive decentralised algorithm based on noncooperative game theory, where neighbouring nodes negotiate among each other to maximize their utility function. We then prove that an increment of the nodes utility corresponds to the same increment of the utility for the whole network. Simulation results show significant performance improvement with respect to existing algorithms.

We propose a source-rate control scheme for streaming video over a wireless channel. The scheme is designed to maximize the quality of the decoded video as perceived at the user-side by resorting on a reduced-reference video-quality estimation approach. The advantage is that the measured video quality, which drives the rate-control algorithm, is obtained after channel errors and error concealment. The rate control algorithm works adjusting the rate on a per-window basis to compensate low-throughput periods with high-throughput periods so as to avoid the “saw” effect that is typically observed in frame-based rate control.

We consider the scenario of a Wireless Sensor Networks (WSN) where the nodes are equipped with a programmable middleware that allows for quickly deploying different applications running on top of it so as to follow the changing ambient needs. We then address the problem of finding the optimal deployment of the target applications in terms of network lifetime. We approach the problem considering every possible decomposition of an application's sensing and computing operations into tasks to be assigned to each infrastructure component. The contribution of energy consumption due to the energy cost of each task is then considered into local cost functions in each node, allowing us to evaluate the viability of the deployment solution. The proposed algorithm is based on an iterative and asynchronous local optimization of the task allocations between neighboring nodes that increases the network lifetime. Simulation results show that our framework leads to considerable energy saving with respect to both sink-oriented and cluster-oriented deployment approaches, particularly for networks with high node densities and non-uniform energy consumption or initial battery charge.

The integration of social networking concepts into the Internet of Things (IoT) has led to the so called Social Internet of Things (SIoT) paradigm, according to which the objects are capable of establishing social relationships in an autonomous way with respect to their owners. The benefits are those of improving scalability in information/service discovery when the SIoT is made of huge numbers of heterogeneous nodes, similarly to what happens with social networks among humans. In this paper we focus on the problem of understanding how the information provided by the other members of the SIoT has to be processed so as to build a reliable system on the basis of the behavior of the objects. We define a subjective model for the management of trustworthiness which builds upon the solutions proposed for P2P networks. Each node computes the trustworthiness of its friends on the basis of its own experience and on the opinion of the common friends with the potential service providers. We employ a feedback system and we combine the credibility and centrality of the nodes to evaluate the trust level. Preliminary simulations show the benefits of the proposed model towards the isolation of almost any malicious node in the network.

This paper addresses the bandwidth management problem in Differentiated-Service-aware Traffic Engineering (DS-TE) architectures. In this context, Bandwidth Constraint (BC) models have to be configured to control the maximum amount of resources per traffic class per link and drive routing decisions to the fulfilment of the QoS requirements. A self-management module is implemented in each node of the network. It monitors the unreserved bandwidth in adjacent nodes and adjusts the local bandwidth constraints so as to reduce the differences in the unreserved bandwidth of neighbor nodes. Accordingly, it smoothes abrupt differences in bandwidth availability along possible paths, which are frequently due to static settings of bandwidth constraints. Due to the distributed nature of the proposed solution, the adjustments can be frequently introduced, allowing for a quick adaptation of the network to the traffic changes. The proposed solution is compared with static bandwidth constraint settings in terms of resulting bandwidth blocking rates, traffic distribution and preemption rate parameters.

The transport stratum in the ITU-T Next Generation Networks (NGN) is expected to provide end-to-end connectivity according to the service requirements, the terminal capability and status of the network resource availability. Whereas mature technologies and protocols, such as DiffServ and MPLS, are available to satisfy these requirements, some issues are still open concerning the capability to provide these services in a dynamic and flexible way. In particular, interoperable and open interfaces are missing at the transport stratum, so that the dynamic activation of distributed application layer services is synchronized with dynamic activation, configuration and monitoring of transport services. This is the challenge addressed in this paper, whose objective is the definition of the NGN Transport Stratum functionalities according to the SOA paradigm and the implementation of the relevant services interfaces to analyze the potentialities of this approach. With the intention to follow the evolutionary approach towards the transition into the NGN networks from the current Internet, this study has been conducted by taking into account the efforts that have been already devoted in the last decade with regard to the definition of the technologies and protocols to build multiservice, QoS-aware and TE-oriented networks solutions. Preliminary experimental results provide some insight on the potentialities of the proposed strategy.

This paper investigates the design of a network management solution that relies on the SOA concepts to access low level network services. The intent is to reduce the gap between the application and the network management, by defining an unique view on the management of the technological assets of an enterprise. In the proposed architecture, the single devices as well as groups of devices are accessed using a web service proxy, which is responsible for dispatching the commands to the devices. To expose those functionalities, the proxy makes use of an object oriented library that hides the inner details of the communications with the physical devices. The resulting solution is made of four levels, which have been defined to simplify the typical management procedures and the implementation of the architecture. An architectural prototype has been developed to evaluate the main advantages, which are: the use of a common formalism for the definition of low level telecommunication services; the use of common interfaces that don't require the operators to know the inner details of each single service; telco services at the higher levels can be obtained as a composition of other services in the lower layers using a composition and coordination logic.

This paper investigates the effects of peer churns in peer-to-peer networks when used to provide video-on-demand services. To evaluate the impact of user behavior in terms of server load and peer satisfaction, three system models have been developed, which are based on: the Gilbert-Elliot chain, the fluidic representation of the user behavior and a queuing analysis of the system. The models are compared evaluating the additional resources that can increase the reliability of P2P network. Simulations show important relationships between playback buffer length, peer request rate, peer average lifetime, and server upload rate.

NGN (next generation network) architectures are constantly evolving towards solutions that allow the operator to provide QoS-guaranteed services in heterogeneous, multi- domain and multi-services networks. In this context, one of the most advanced technologies is DiffServ-aware Traffic Engineering. It performs traffic engineering (TE) in a differentiated service environment by applying routing constrains with class granularity. This approach relies on the definition of an efficient bandwidth constraint model to allocate the available link resources in a per class type (CT) basis according to a certain set of resource utilization constraints. In this work, we describe a genetic algorithm (GA) that is aimed at finding the optimal bandwidth constraint settings for each link in the network. According to the TE objectives, the optimality is expressed in terms of a new cost function which weighs both the uniformity in network resource utilization and the average label switching path (LSP) length. The effectiveness of the proposed solution is analyzed comparing its bandwidth blocking rate and QoS results with those of two default bandwidth constraint configurations.

The last few years have been characterized by a rapidly growing market share of Voice over IP (VoIP) providers against traditional voice service operators, thanks to the low-cost of the packet-based technologies and the reliability of the current (wired) IP networks. We believe that a similar success is expected to happen in mobile ad hoc networks (MANETs), which may offer a good platform for the fast deployment of VoIP mobile networks. However, efforts must be made to improve performance before MANETs can be used for this purpose. One of the main limitations is related to the highly variability of the network topology and channel behavior, which heavily influences the service quality due to route losses and significant delay variations. In this paper, we propose a strategy where these impairments are jointly addressed. The source is responsible for jointly selecting the transmission paths and adjusting the playout delay, with an adaptive inter-talkspurt approach. These tasks are accomplished on the basis of historical data on network connectivity and transmission delays, and are driven by a quality-based approach. The collection of statistics of the network status relies on the QOISR routing algorithm, whereas the voice quality is measured by means of the ITU-T E-Model.

NGN (Next Generation Network) architectures are constantly evolving towards solutions that allow the operator to provide QoS-guaranteed services in heterogeneous, multi-domain and multi-services networks. In this context, DiffServ-aware Traffic Engineering (DS-TE) is one of the most advanced technologies, which performs traffic engineering (IE) in a differentiated service environment by applying routing constrains with class granularity. This approach requires the definition of an efficient bandwidth constraint model to allocate the available link resources in a per Class Type (CT) basis, which influence both resource utilization and provided QoS. We analyze this problem and propose an algorithm to find the optimal mapping of service types into Class Types and the optimal bandwidth allocation for each of these. Herein, the optimality is expressed in terms of a cost function weighting both the expected satisfaction of the quality of service targets and the network resource utilization. The effectiveness of the proposed solution is analyzed considering a real context with expect near future combination of service requests from the end-users.

Source-rate control is an important issue in video streaming applications, particularly for wireless networks where channel resources are often shared among a variable number of stations using a contention-based access mechanism. In this context, the resulting throughput available for the video server has been demonstrated to be bursty, which is a feature that makes high-quality video streaming quite difficult. On the basis of this observation, we propose a rate control algorithm that works adjusting the rate on a per-window basis to compensate low-throughput periods with high-throughput periods so as to avoid the "saw" effect that is typically observed in frame-based rate control. The time axis is divided into windows of fixed size and rate changes are introduced only at the beginning of each window with the aim of keeping the probability of playback buffer starvation lower than a desired threshold during the entire current window. To achieve this objective, the algorithm makes use of a short-term prediction of the network delay using historical data. Simulations proved the efficiency of algorithm when controlling the starvation probability while avoiding the introduction of sudden changes in the source rate.

We propose a joint source-rate/channel-code control scheme for streaming video over a wireless channel. The scheme is designed to maximize the achievable source rate while guaranteeing an upper bound on the probability of starvation at the playback buffer. It can be applied to both one-way and interactive video communications. Rate control is performed adaptively on a per-cycle basis, where a cycle consists of a "good" channel period and the ensuing "bad" period. This cycle-based approach has two advantages. First, it reduces the fluctuations in the source bit rate, ensuring smooth variations in video quality. Second, it makes it possible to derive simple expressions for the starvation probability at the playback buffer, which we use to determine the optimal source rate and channel code for the good and bad periods of the subsequent cycle

Power Management mechanisms are widely adopted in Wireless LANs to achieve appreciable power saving. In DCF iBSS networks, all stations in doze mode with pending frames are awaken by the AP at the beginning of next beacon interval. Such stations then switch to the active mode for the reception of the frames. In this work, we propose a different power management technique based on giving the AP the power of deciding which stations with pending frames to wake up. Indeed, there are several circumstances with high channel traffic in which it is better to defer the transmission so as to reduce the expected energy consumption. The AP decision is taken in view of the energy consumption due to collisions and transmissions together with the introduced latency. Through simulations, we show the performance of the proposed method, which may lead to an overall energy saving of about 40 % respect to the standard Power Management.

This work focuses on the group multicast routing problem. The major contribution of the paper to this problem is twofold. Firstly, the use of the genetic algorithms (GA) is proposed for solving the complexity problem inherent to the packing of multiple sessions. Secondly, a novel cost function is proposed, weighting in a single expression both network bandwidth allocation and provided one-way delay. The proposed function is guided by few parameters that can be easily tuned during traffic engineering operations; an appropriate setting of these parameters allows the operator to configure the desired balance between network resource utilization and provided QoS in terms of transmission delay. Experimental results are compared with those of a heuristic algorithm that provides a lower bound for the optimization problem. This highlights that the proposed method brings to a strong reduction in the processing time.

In IEEE 802.11 infrastructure LANs with Distributed Coordination Function (DCF), the power saving algorithm works as follows: the stations with no frames to transmit may switch to the doze mode to save power; all these stations are informed by the Access Point (AP) at the beginning of each beacon interval if there are pending frames; these stations then start contending the channel to receive these pending frames from the AP, if any. In this work, we propose a different strategy based on giving the AP the power of deciding which stations with pending frames to notify. Indeed, there are several circumstances with high channel traffic in which it is better to defer the transmission so as to reduce the expected energy consumption. The reduction in energy consumption is obtained at the expense of an increase in the transmission latency. The AP notification decision is then taken in view of the total average energy consumption and the introduced latency. Simulations have shown that the performance of the proposed method may lead to an overall energy saving of about 63% respect to the standard algorithm.

To combat jitter problems in voice streaming over packet networks, playout buffering algorithms are used at the receiver side. Most of the proposed solutions rely on two main operations: prediction of delay statistics for future packets; setting of the end-to-end delay so as to limit or avoid packet losses. In recent years, a new approach has been presented, which is based on using a quality model to evaluate the impact of both packet loss and delay on the voice quality. Such a model is used to find the buffer setting that maximizes the expected quality. In this paper, we present a playout buffering algorithm whose main contribution is the extension of the new quality-based approach to the case of voice communications affected by bursty packet losses. This work is motivated by two main considerations: most of IP telephony applications are characterized by bursty losses instead of random ones; the human perception of the speech quality is significantly affected by the temporal correlation of losses. To this purpose, we make use of the extensions proposed in the ETSI Tiphon for the ITU-T E-Model so as to incorporate the effects of loss burstiness on the perceived quality. The resulting playout algorithm estimates the characteristics of the loss process varying the end-to-end delay, weights the loss and the delay effects on the perceived quality, and maximizes the overall quality to find the optimal setting for the playout buffer. The experimental results prove the effectiveness of the proposed technique.

We address the problem of real-time estimation of multifractal parameters of network traffic. The algorithm accuracy is the major concern in the proposed algorithm. From a statistical point of view, the higher the number of samples used in the estimation, the more accurate the results. However, the network traffic in long intervals of time may have a heterogeneous scaling behavior, which would make the estimation results meaningless. We then propose an adaptive strategy that adjusts the length of the estimation interval on the basis of the local traffic features so as to extend the number of samples as much as the traffic behavior is deemed to be stationary. The development of this strategy relies on an analysis of the variability of multifractality over time in real traffic traces. Simulation results show that the proposed algorithm is characterized by a higher accuracy with respect to a fixed approach.

In this paper, we present methods that can be used to conceal errors in corrupt motion JPEG2000 codestreams. Motion JPEG2000 is an intra-frame compression technique and the proposed concealment methods utilize motion compensation to conceal errors. The simulation results indicate that the proposed methods can yield over 10 dB improvement in PSNR.

Transmission errors in JPEG2000 can be grouped into three main classes, depending on the affected area: LL, high frequencies at the lower decomposition levels, and high frequencies at the higher decomposition levels. The first type of errors are the most annoying but can be concealed exploiting the signal spatial correlation like in a number of techniques proposed in the past; the second are less annoying but more difficult to address; the latter are often imperceptible. In this paper, we address the problem of concealing the second class or errors when high bit-planes are damaged by proposing a new approach based on the theory of projections onto convex sets. Accordingly, the error effects are masked by iteratively applying two procedures: low-pass (LP) filtering in the spatial domain and restoration of the uncorrupted wavelet coefficients in the transform domain. It has been observed that a uniform LP filtering brought to some undesired side effects that negatively compensated the advantages. This problem has been overcome by applying an adaptive solution, which exploits an edge map to choose the optimal filter mask size. Simulation results demonstrated the efficiency of the proposed approach.

Simulations represent a very powerful means for validating network designs that help telecommunication operators to predict network performance. In this context, two important issues have to be carefully considered: a speedup technique has to be used to reduce the long simulation times; the traffic models should be able to reproduce the burstiness observed in real traces. In this paper, we propose a new solution to these problems based on the application of the importance sampling (IS) theory to the power on-power off model, which can accurately reproduce the self-similar nature of the real traffic. The accuracy and efficiency of the technique have been validated by extensive experiments.

We propose a novel source-rate control scheme for streaming video over wireless channels. This scheme is designed to maximize the bit rate at the encoder while guaranteeing an upper bound on the probability of starvation at the playback buffer. Channel dynamics are captured using the Gilbert-Elliot model, with alternating good and bad periods. In contrast to previous approaches, rate control in our scheme is performed adaptively on a per-cycle basis, where a cycle consists of one good period and the ensuing bad period. The cycle-based approach has two advantages. First, it reduces the fluctuations in the source bit rate, ensuring smooth variations in video quality and avoiding the "saw" effect that is typically observed in frame-by-frame rate control. Second, it makes it possible to derive a closed-form expression for the starvation probability, which we use to determine the optimal source bit rates for the good and bad periods of the following cycle. Because of its low computational complexity, the proposed scheme is attractive for real-time video streaming. Simulations are carried out to assess the performance of the scheme and study the interactions among various system parameters.

In this paper, we address the problem of concealing high-frequencies errors at the lower decomposition levels in JPEG2000, by proposing a new approach based on the theory of projections onto convex sets. The error effects are masked by iteratively applying two important procedures: low-pass (LP) filtering in the spatial domain and restoration of the uncorrupted wavelet coefficients in the transform domain. It has been observed that a uniform LP filtering brought to some undesired side-effects that negatively compensated the advantages. This problem has been overcome by applying an adaptive solution exploiting an edge map to choose the optimal filter mask dimension. Simulation results demonstrated the efficiency of the proposed approach.

In Internet-protocol (IP) telephony, problems of transmission delay variations are frequently addressed with adaptive dejitter buffering techniques. These are aimed at setting the buffer dimension so as to limit the packet end-to-end delay, the total packet loss, or both together. The selection of delay and loss limits is of key importance for the resulting conversational quality. This problem is addressed in this letter, whose main contribution is the introduction of a perceptually motivated optimality criterion that allows the receiver to automatically balance packet delay versus packet loss. In the proposed approach, the dejitter buffer size is adaptively set, and the adopted criterion relies on the use of a simplified version proposed by Cole and Rosenbluth of the conversational-quality International Telecommunication Union (ITU) Telecommunication Standardization Sector (ITU-T) E-Model.

Telecommunication operators frequently evaluate network performance to validate and optimise network designs by simulation. There is a big problem in this context: simulations often require a long run-time to get accurate results. We address this problem, presenting a new traffic scaling approach to reduce the number of events to be simulated. The proposed method is based on the idea of scaling link capacity and input traffic by a common scaling factor in order to speed up simulations. We analyse the accuracy of this technique for a multiservice asynchronous transfer mode (ATM) network technology, even if such an approach can be applied to other transport technologies. Experiments show good accuracy and high speedup values compared to alternative solutions.

Current standards for video compression achieve good performances in terms of data compaction and signal-to-noise ratio of the decoded signal. Nevertheless, there are some known problems concerning the visual quality of reconstructed images, which can be partially solved using appropriate post-processing algorithms. The paper proposes a new adaptive anisotropic filter (AAF) that aims to unify the treatment of different sources of perceptive distortion in MPEG sequences. The process is driven by a local classification of blocks and single pixels of decoded frames, taking into account several parameters (distribution of DCT coefficient energy, presence of sharp variations, spatial position of DCT block boundaries). Experimental results show that the proposed algorithm outperforms existing enhancement approaches, in particular when constraints on complexity and real-time processing are compelling.

In this paper, the problem of bit-errors recovery in JPEG2000 images transmission is addressed, with particular attention to errors in the high-frequency components. In the HL and LH sub-bands, differently from the LL band, interpolation based recovering results tend to be often ineffective when more than a few adjacent wavelet coefficients are missing. The solution proposed is then to apply a wavelet patch repetition procedure by predicting the similitude between the contour structure in the damaged area and its surroundings. To this aim, the correlation in the spatial structure, that is the contour information, existing between different sub-bands has been analyzed and exploited. Accordingly, the patch used to conceal the corrupted region is that obtained by minimizing a correlation measure with the spatial structure extracted from an adjacent not corrupted subband. Objective and subjective improvements have been obtained.

This paper presents a new technique for video segmentation and tracking. As most of segmentation techniques it consists on an initial model generation process with a subsequent object tracking phase. The model generation is accomplished by means of a combination of temporal and spatial transitions detection approaches. The novelty of the method is that these approaches are performed block-by-block. This has the advantages to reduce problems relevant to the object connectivity and to drastically decrease the algorithm computational complexity respect to a pixel-by-pixel processing procedure. According to the proposed strategy, edged blocks are firstly extracted in an active region selected by the user. From these, the subset of blocks that represents the object contour is selected by minimizing a cost function that exploits a multiple edged block feature vector: motion, smoothness, continuity, strongness and position. The tracking task is then performed by estimating the model blocks motion. Experiments are presented that show comparable results accuracy respect to existing segmentation techniques while requiring a reduced computational complexity.

The transmission of block-coded visual information over packet networks introduces fidelity problems in terms of data losses, which result in wrong reconstruction of block sequences at the decoder. Concealment techniques aim at masking the visual effect of these errors, by exploiting either spatial or temporal available information. Both temporal and spatial approaches present drawbacks: the first is in general inefficient in handling complex or fast objects' motion, while the second is computationally expensive and is not able to recover high-frequency contents and small details. In this paper, a new solution is proposed that combines temporal and spatial approaches. The technique first replaces the lost block with the best matching pattern in a previously decoded frame (BMA), using the border information, and then applies a mesh-based warping (MBW) that reduces the artifacts caused by fast movements, rotations or deformations. The first step is achieved by a fast matching algorithm, for a high precision is not needed, while the second step uses an affine transform applied to a deformable mesh structure. Experimental results show that significant improvements can be achieved in comparison with traditional spatial or temporal concealment approaches, in terms of both subjective and objective reconstruction quality.

Most widespread video coding algorithms (such as MPEG, H.261, H.263) employ DCT coding for data compression but introduce annoying artefacts due mainly to the independent quantization of the coefficients in each block, that are especially visible at medium and low bitrates. Within this framework, post-processing appears to be a practical solution for visual enhancement of compressed video. In this paper, an adaptive anisotropic spatial-variant FIR filtering procedure is proposed. The filter kernels are selected on the basis of a pixel classification procedure that performs a block-DCT coefficients energy analysis and an edge extraction. The analysis of the transform coefficients matrix allows one to extract information about spatial characteristics, while the edge information provide the basis for the estimation of the position of the local visual artifact. Accurate filtering results were obtained during experiments that outperform those obtained with other existing approaches.