Quality of Experience

Recently, the use of heterogeneous small-cell networks to offload traffic from existing cellular systems has attracted considerable attention. One of the significant challenges in heterogeneous networks (HetNet) is cross-tier interference, which becomes significant when macro-cell users (MUE) are in the vicinity of femtocell base stations (FBS). Indeed, the femtocell will cause significant interference to MUEs on the macrocell downlink (DL) while MUEs will induce hefty interference to the femtocell on the macrocell uplink (UL). Substantial work has focused on offloading and interference mitigation in HetNets; yet, none of them has considered the impact of cross-tier interference on quality of service (QoS), quality of experience (QoE). This paper proposes the Quality Efficient Femtocell Offloading Scheme (QEFOS) that selects the users most affected by the interference encountered and offloads them to nearby FBSs. QEFOS testing shows substantial improvements in terms of QoS and QoE perceived by users in heavy cross-tier interference scenarios in comparison with alternative approaches. In particular QEFOS's impact on throughput, packet loss ratio (PLR), peak-to-signalnoise ratio (PSNR), and structural similarity identity matrix (SSIM) was assessed.

Purpose-The Video Quality Metric (VQM) is one of the most used objective methods to assess video quality, because of its high correlation with the human visual system (HVS). VQM is, however, not viable in real-time deployments such as mobile streaming, not only due to its high computational demands but, because, as a Full-Reference (FR) metric, it requires both the original video and its impaired counterpart. In contrast, No-Reference (NR) objective algorithms operate directly on the impaired video and are considerably faster, but loose out in accuracy. This research studies how differently NR metrics perform in the presence of network impairments. Design/methodology/approach-We assess 8 NR metrics, alongside a lightweight FR metric, using VQM as benchmark in a self developed network-impaired video dataset. Our study covers a range of methods, a diverse set of video types and encoding conditions, and a variety of network impairment test-cases. Findings-We show the extent by which packet loss affects different video types, correlating the accuracy of NR metrics to the FR benchmark. Our study helps identifying the conditions under which simple metrics may be used effectively and indicates an avenue to control the quality of streaming systems. Originality/value-Most studies in literature have focused on assessing streams that are either unaffected by the network (e.g., looking at the effects of video compression algorithms) or are affected by synthetic network impairments (i.e. via simulated network conditions). We show that when streams are affected by real network conditions, assessing QoE becomes even harder, as the existing metrics perform poorly.

Satisfying the requirements of devices and users of online video streaming services is a challenging task. It requires not only managing the network Quality of Service but also to exert real-time control, addressing the user's Quality of Experience (QoE) expectations. QoE management is an end-toend process that, due to the ever-increasing variety of video services, has become too complex for conventional 'reactive' techniques. Herein, we review the most significant 'predictive' QoE management methods for video streaming services, showing how different machine learning approaches may be used to perform proactive control. We pinpoint a selection of the best suited machine learning methods, highlighting advantages and limitations in specific service conditions. The review leads to lessons learned and guidelines to better address QoE requirements in complex video services.

When dealing with networks, performance management through conventional Quality of Service (QoS) based methods becomes difficult and is often ineffective. In fact, quality emerges as an end-to-end factor, for it is particularly sensitive to the end-user perception of the overall service, i.e. the user's Quality of Experience (QoE). However, the two are not independent from each other and their relationship has to be studied through metrics that go beyond the typical network parameters. To better explore the value of assessing QoE alongside QoS in high-speed, lossy networks, this paper presents an experimental methodology to understand the relation between network QoS onto service QoE, with the aim to perform a combined networkservice assessment. Using video streaming services as the testcase (given their extended usage nowadays), in this work, we provide studies on three network-impaired video-sets with the aim to provide a comprehensive evaluation of the effects of networks on video quality. First, the ReTRIeVED video set provides the means to understand the most impairing effects on networks. Furthermore, it triggered the idea to create our own sets, specialized in the most impairing conditions for 2D and 3D: the LIMP Video Quality Database and the 3D-HEVC-Net Video Quality Database. Our study and methodology are meant to provide service providers with the means to pinpoint the working boundaries of their video-sets in face of different network conditions. At the same time, network operators may use our findings to predict how network control policies affect the user's perception of the service.

Evaluating quality of experience in video streaming services requires a quality metric that works in real time and for a broad range of video types and network conditions. This means that, subjective video quality assessment studies, or complex objective video quality assessment metrics, which would be best suited from the accuracy perspective, cannot be used for this tasks (due to their high requirements in terms of time and complexity, in addition to their lack of scalability). In this paper we propose a light-weight No Reference (NR) method that, by means of unsupervised machine learning techniques and measurements on the client side is able to assess quality in real-time, accurately and in an adaptable and scalable manner. Our method makes use of the excellent density estimation capabilities of the unsupervised deep learning techniques, the restricted Boltzmann machines, and light-weight video features computed just on the impaired video to provide a delta of quality degradation. We have tested our approach in two network impaired video sets, the LIMP and the ReTRiEVED video quality databases, benchmarking the results of our method against the well-known full reference metric VQM. We have obtained levels of accuracy of at least 85% in both datasets using all possible cases.

Evaluating quality of experience in video streaming services requires a quality metric that works in real time and for a broad range of video types and network conditions. This means that, subjective video quality assessment studies, or complex objective video quality assessment metrics, which would be best suited from the accuracy perspective, cannot be used for this tasks (due to their high requirements in terms of time and complexity, in addition to their lack of scalability). In this paper we propose a light-weight No Reference (NR) method that, by means of unsupervised machine learning techniques and measurements on the client side is able to assess quality in real-time, accurately and in an adaptable and scalable manner. Our method makes use of the excellent density estimation capabilities of the unsupervised deep learning techniques, the restricted Boltzmann machines, and light-weight video features computed just on the impaired video to provide a delta of quality degradation. We have tested our approach in two network impaired video sets, the LIMP and the ReTRiEVED video quality databases, benchmarking the results of our method against the well-known full reference metric VQM. We have obtained levels of accuracy of at least 85% in both datasets using all possible cases.

Current mobile devices and streaming video services support high definition (HD) video, increasing expectation for more contents. HD video streaming generally requires large bandwidth, exerting pressures on existing networks. New generation of video compression codecs, such as VP9 and H.265/HEVC, are expected to be more effective for reducing bandwidth. Existing studies to measure the impact of its compression on users' perceived quality have not been focused on mobile devices. Here we propose new Quality of Experience (QoE) models that consider both subjective and objective assessments of mobile video quality. We introduce novel predictors, such as the correlations between video resolution and size of coding unit, and achieve a high goodnessof-fit to the collected subjective assessment data (adjusted Rsquare >83%). The performance analysis shows that H.265 can potentially achieve 44% to 59% bit rate saving compared to H.264/AVC, slightly better than VP9 at 33% to 53%, depending on video content and resolution.

The Quality of Experience (QoE) is an irreplaceable metric for evaluating the perceived quality of consumers of multimedia content. Due to the subjectiveness of QoE the most suitable way to measure it is by executing subjective studies. However, executing subjective studies is a complex and expensive process. Careful recreation of the viewing conditions is necessary, and a strict selection of the test subjects is required based on many criteria. This is why solutions are often found in various objective methodologies for measuring the QoE of multimedia. These solutions even though more practical are less accurate and cannot reflect the user expectations. In this work we present a method for building QoE prediction models using machine learning techniques from continuous real-time customer feedback, i.e., during the service execution. This online learning approach builds and adapts prediction models that estimate the QoE based on given Quality of Service metrics from real-time user feedback and does not require apriori execution of subjective studies.

Abstract: Peer-to-peer (P2P) is a promising technology for video streaming, and offers advantages in terms of re-configurability and scalability. It gains advantage from and share the resources owned by the end-users who are distributed around the Internet. P2P has shown an alternative solution for the traditional Client-Server approach limitations. However, due to the churn of peers, issues of video quality arise such as packet loss. This in turn degrades the QoS, then the QoE. Moreover, in current networking conditions, congestions ...

Quality of Experience (QoE) is a metric that quantifies the quality experienced while using a service. This is a subjective metric that is crucial for determining the level of satisfaction with the quality of services, such as multimedia streaming or games. Because of its subjective character, QoE is estimated accurately by executing complex and cumbersome subjective studies, or not as accurately by using objective signal distortion metrics. In order to get the accuracy of the subjective approach and the practicality of the objective, we ...

Estimating perceived quality of video is typically done by gauging the user's response on an absolute scale of ratings (excellent, good, fair, poor and bad). However, the internal representation of these adjectives to the stimuli varies significantly in different people. Even though the goal is to make an absolute estimate of the perceived quality, these questions reveal merely relative tendencies due the incorporated bias and variability in the responses. We present results from quality assessment based on estimates of relative quality distances between samples, by asking the question in the form or comparison rather than rating. This, two-alternative forced choice method scales the differences in a form of psychometric function, which presents the utility of the perceived quality on a measurable objective value. We argue that this relativistic mapping with low variance is more useful in video delivery because it offers an accurate way to optimize the resources.

     Streaming of video over wireless heterogeneous networks coping with the problem of packet loss which affects the perceived video quality. The service providers usually use the Peak Signal to Noise Ratio PSNR as a metric measure for the quality of their provided service. So they use the quality of service QoS of the network as a sign on the quality of their presented service. The QoS deal with the objective tests of the provided service, which mean the measure of PSNR of the presented objects. The presented objects may not get the satisfaction of the network users due to many factors although that the PSNR of the used service is enough for presenting the service. Recently the service providers use the Quality of Experience QoE term which deal with the subjective test of the presented object (i.e. the user satisfaction measure). In this paper we propose a new model to identify the importance or the significance of the role of the QoE assessment for the service providers. To verif...

Practical implementation of new P2P streaming systems requires a lot of coding and is often tedious and costly, slowing down the technology transfer from the research community to real users. GRAPES aims at relieving this burden, and speed-up application development, by providing a set of open-source components conceived as basic building blocks for building new P2P streaming applications. GRAPES is designed to be usable in different environments and situations, to have a minimal set of pre-requisites and dependencies, and not to impose any particular constraints on applications using it. The development of streamers in the Napa-Wine project [1] has shown that using GRAPES P2P streaming applications with different characteristics can be developed by only writing a small amount of glue code that connects the desired functionalities and dictates the architecture of the streamer.

This work presents a visual telepresence system that captures and projects panoramic images into immersive environments in real-time. A number of images are captured with a ring of cameras (a special mounting of multiple digital cameras that captures synchronized images of a 360 degrees scene). These images are processed by a PC cluster before their transmission. A panoramic image is locally rendered by the proposed optimized stitching method that computes a positioning map for all pixels. Then, a complete cylindrical image is sent over the Internet to a multi-projection virtual environment called CAVERNA Digital®. Since the panoramic view is projected in natural size onto the screens of the virtual environment, the user has the sensation of being present at the remote scene.

Virtual Reality (VR) and Augmented Reality (AR) are revolutionizing immersive 360°video experiences across education, entertainment, and cultural exploration. This paper introduces a Smart Video Encoder (SVE) system, comprising server-and user-side Virtual Network Functions (VNFs), for adaptive compression and transmission of 360°video streams over 5G/6G networks. Leveraging a Supervised Machine Learning (SML) Video Analyzer, the system enhances userperceived quality by prioritizing key regions, such as artistic content. Using hierarchical tile-based compression, the SVE adjusts compression dynamically based on real-time user behavior and video content. Results show its effectiveness in maintaining high-quality experiences under bandwidth constraints, particularly for applications like virtual museum tours.

Virtual reality (VR) cloud gaming faces significant challenges in terms of latency and bandwidth consumption, particularly in softwarized 5G and beyond (5G&B) networks. Ensuring an immersive experience on low-end devices, such as smartphones and cardboard-like headsets, requires innovative solutions to mitigate delay effects and optimize resource usage. This research focuses on enhancing VR cloud gaming performance by leveraging Digital Twins (DTs) and hierarchical compression techniques. The proposed system adopts a client-server architecture, where computationally intensive tasks are offloaded to the server, minimizing processing requirements on the client side. A key contribution is a novel delay estimation and management framework that employs DTs to synchronize video streaming and user input, reducing the impact of latency. Additionally, viewport-based hierarchical compression is applied to optimize bandwidth consumption while maintaining high perceived quality. The proposed architecture is evaluated through extensive experiments analyzing the effects of input delay, delay fluctuations, and compression methods on user performance and perceived quality. Results demonstrate that the system effectively mitigates adverse network conditions, ensuring a seamless and immersive VR gaming experience even in suboptimal environments.

The entertainment and gaming industries continue to evolve, so they are becoming increasingly reliant on advanced network capabilities to deliver immersive, real-time experiences. In-network computing (INC) is a transformative paradigm in the design of the network architecture in 6G networks. It facilitates the offloading of computational tasks from devices to edge nodes and central servers, enabling faster data processing in network nodes and reducing latency and response times for high-demand applications, enhancing efficiency. This way, it will be possible to provide the final users with advanced applications like Augmented Reality (AR) and Virtual Reality (VR). In this paper we introduce a 6G Network Infrastructure testbed designed for application execution and testing, supporting INC, and we present an Adaptive 360°Video Streaming service using INC facilities. The testbed has been designed to represent a vertical application for SLICES-RI and SUNRISE-6G projects. Some numerical results will assesses the performance of this dynamic video streaming system.

Nowadays, Virtual Reality (VR) is becoming a crucial technology in various sectors, emerging as one of the fastest-growing market segments in recent years. Moreover, continuous advancements in networking and multimedia technologies are increasing the interest regarding several VR applications. Given that 360°video transmission is bandwidth hungry, video compression plays a key role for the deployment of applications based on it. Since users have a partial view of the whole 360°image stream, that is the portion they are focusing, while the part that is behind them is not seen, a widely adopted way to compress this kind of stream is by using a differentiated compression, with higher quality for the image areas that mostly influence the perceived quality. However, at the best of our knowledge, perceived quality has been never correlated to the grade of interaction between the user and the viewed scene, and to the network delay. To this purpose, the main

In the context of QoE management, network and service providers commonly rely on models that map system QoS conditions (e.g., system response time, paket loss, etc.) to estimated end user QoE values. Observable QoS conditions in the system may be assumed to follow a certain distribution, meaning that different end users will experience different conditions. On the other hand, drawing from the results of subjective user studies, we know that user diversity leads to distributions of user scores for any given test conditions (in this case referring to the QoS parameters of interest). Our previous studies have shown that to correctly derive various QoE metrics (e.g., Mean Opinion Score (MOS), quantiles, probability of users rating "good or better", etc.) in a system under given conditions, there is a need to consider rating distributions obtained from user studies, which are often times not available. In this paper we extend these findings to show how to approximate user rating distributions given a QoS-to-MOS mapping function and second order statistics. Such a user rating distribution may then be combined with a QoS distribution observed in a system to finally derive corresponding distributions of QoE scores. We provide two examples to illustrate this process: 1) analytical results using a Web QoE model relating waiting times to QoE, and 2) numerical results using measurements relating packet losses to video stall pattern, which are in turn mapped to QoE estimates.

In mobile cloud computing, offloading mobile applications to close remote servers appears as a straightforward solution to overcome mobile terminals processor and battery limitations. Remote execution leverages the high computation capacity of the server to enrich user experience and extend battery autonomy through energy savings. However, application offloading is energy efficient only under various conditions. For that purpose, we propose an original algorithm called MAO (Mobile Application's Offloading) triggered by two conditions : the current CPU load and State of Charge (SoC) of the battery. On the basis of various traffic scenarios mixing interactive and delay tolerant mobile applications, we study through numerical simulations the efficiency of the MAO algorithm and assess its performance in terms of rejected jobs and the amount of energy savings achieved. Rejected jobs are those unable to meet user quality of experience (QoE) and/or energy efficiency requirements. Evaluations on simulated workloads show that both traffic loads and user's radio mobile environment have a direct impact on the efficiency of the MAO algorithm.

The proliferation of mobile video streaming necessitates efficient HTTP Adaptive Streaming (HAS) to optimize video quality and ensure equitable distribution among clients. Client-side ABR often degrades QoE and yields unfair bitrate allocation under mobile variability. We frame this challenge as a multi-objective optimization and propose EQAH, a Lyapunov-based, edge-assisted quality adaptation heuristic. Executed at the edge node, it optimizes video quality and fairness using a novel throughput estimator, Weighted Harmonic Exponential Averaging (WHEA), which improves robustness and accuracy under dynamic conditions. The algorithm runs once per segment in O(1) time (tens of µs) with fresh buffer/throughput inputs, meeting real-time constraints. Evaluated on public 4G LTE and 5G traces, EQAH outperforms existing edge-assisted methods in both QoE and fairness across diverse mobility scenarios.

Transmission errors are the main cause of degradation of the quality of real broadcasted video services. Therefore, knowing their impact on the quality of experience of the end users is a crucial issue. For instance, it would help to improve the performance of the distribution systems, and to develop monitoring tools to automatically estimate the quality perceived by the viewers. In this paper we validate a subjective evaluation approach specifically designed to obtain meaningful results of the effects of degradations caused by transmission errors. This methodology has been already used in our previous works with monoscopic and stereoscopic videos. The validation is done by comparing the subjective ratings obtained for typical transmission impairments with the proposed methodology and with the standard method Absolute Category Rating. The results show that the proposed approach could provide more representative evaluations of the quality of experience perceived by end users of conventional and 3D broadcasted video services.

In 5G NR, robust data delivery depends on the joint operation of retransmission mechanisms at Layer 2, specifically RLC ARQ and MAC HARQ. Two key parameters, tPollRetransmit and maxRetxThreshold, govern the RLC AM retransmission behavior. tPollRetransmit defines the timer for retransmission polling, directly affecting how quickly lost PDUs are detected, while maxRetxThreshold sets the maximum number of allowed retransmissions before declaring a radio link failure. Their configuration must be carefully aligned with MAC HARQ processes, which provide faster, physical-layer retransmissions with limited buffer capacity. Improper parameter tuning may lead to excessive retransmissions, spurious RLF triggers, or inefficient radio resource use. Conversely, well-balanced settings minimize delay, optimize packet recovery, and ensure seamless interaction between HARQ and ARQ layers. Simulation and field results indicate that throughput is maximized when RLC timers and retransmission thresholds are harmonized with HARQ round-trip times and scheduler behavior. Ultimately, the correct interplay of tPollRetransmit and maxRetxThreshold is crucial to achieving high throughput, low latency, and reliable 5G network performance.

this paper presents the Mobile Services Living Laboratory -a practical approach for evaluating service provision in cellular networks. Our approach promotes an endto-end view of the system by providing an effective means to store information from both terminal and network sides, together with integrated mechanisms for retrieving feedback on user-perceived service quality. The current implementation of the Living Laboratory is described and evaluated. Further, we show how the Living Laboratory is used to investigate both the effectiveness of context-aware opportunistic content delivery schemes in cellular networks, and the coexistence between M2M and usergenerated traffic.

This paper presents a subjective image quality experiment design that specifically targets understanding consumer perception of visual quality. With this consumer-oriented goal in mind, images were captured from a large number of representative consumer devices. This design choice is fundamentally different from previous work in the field, in that it is not based on simulated software impairments, but rather on naturally occurring artifacts due to the device and conditions. New subjective questions were posed, which allowed exploration of differences in the quality of experience (QoE) provided by various devices. This paper presents the experimental design implemented to capture ratings and feedback on these consumer devices. The study is analyzed to highlight and demonstrate the unique capabilities provided by this method.

Over the last years, the increasing number of mobile devices, their capabilities and the access in wireless network have created an enormous rise on wireless traffic demand and use. Wireless networks often experience "periods of severe impairments", causing severe degradation to the performance of the service running on wireless devices and to the respective user experience. However, the impact of the network performance on the quality of experience (QoE) for various services is not understood in depth. Thus, assessing the impact of different network conditions and system parameters on the user experience is important for improving the telecommunication services. In general, depending on the type of service and the context, the QoE can be affected by various techno-socio-economic-cultural-psychological parameters, e.g., by the user preferences with respect to QoE and price, willingness-to-pay, and intrinsic indicators towards a service provider (e.g., brand name, perceived value, reliability), its content (e.g., richness, diversity, searching mechanisms), and even integration with other popular services (e.g., social networking applications). In the related work, the majority of efforts aim to characterize and predict the user experience, analyzing various types of measurements often in an aggregate manner. Our group developed the uQoE, a modular framework that includes monitoring and data collection tools (uQoE tracker) and algorithms for user-centric analysis and prediction of the QoE (MLQoE prediction algorithm) in the context of video streaming service. The uQoE tracker collects network and system measurements as well as feedback from the user. The MLQoE employs several machine learning (ML) algorithms and tunes their hyper-parameters, given as input the uQoE tracker collected measurements. It dynamically selects the ML algorithm that exhibits the best performance and its parameters automatically based on the input (e.g., network and system metrics). In this thesis, we applied the uQoE for analyzing and predicting the QoE of the video streaming service in the context of two field studies, one performed in the production environment of a large telecom operator and the other at our Institute. The analysis indicated the parameters with the dominant impact on the perceived QoE and revealed that the QoE may vary across users. This motivates the use of customized adaptation mechanisms in video streaming to address the degradation in network performance. The MLQoE results in fairly accurate predictions. iii the mobile computing group for their friendship and support during these years. Evripidis, Nikos, Vasilis, Haris and George thank you all for the helpful discussions, the encouragement, and the great atmosphere. A dedicated thank you to my closest friend Tasos and my girls Konstantina, Elena, Sofia, Despoina and Katerina for being always there to listen and for always providing their advice and support. Last, but definitely not least, I would like to thank my family for their enormous and selfless support during these years. Σίσσυ, μαμά και μπαμπά σας ευχαριστώ και σας αγαπώ πολύ.

This paper presents the u-map, a user-centric crowdsourcing recommendation system that enables mobile users to select the best service provider in a certain region. Via u-map, users upload information about their profile, quality of experience (QoE) feedback for a service, network measurements, and position in the database of u-map. In our earlier work, we analyzed the performance of u-map, in terms of power consumption, responsiveness, and scalability. To understand how users assess such systems in general, and how they perceive the performance of u-map and its services specifically, we performed a field study. The study evaluates the GUI of the u-map client and the impact of u-map on the discovery of new networks for improving the QoE of VoIP calls. The paper presents a methodology for performing such studies, analyzes the collected measurements, models the user satisfaction about u-map, and highlights the main findings.

Wireless sensor networks have a wide range of applications and can be used for various purposes in different environments. Wireless sensors are small and they are mostly using battery supplies. Communication, sensing and computing have the most influence on performance and power consumption of wireless sensor networks. In this paper we describe layered architecture of wireless sensor network protocol stack and discuss the advantages of cross-layer approach. The articles that are using a cross-layer approach for improving performances and energy preservation in wireless sensor networks are reviewed and state of the art is presented. One of the fundamental problems for wireless sensor networks is how to systematically make a performance evaluation. Because there are plenty of different wireless sensor network optimization methods there should be a universal benchmark suite. We have proposed some useful guidelines for its development.

The emergence of social networks has triggered the evolution of e-commerce to what is now known as socialcommerce (s-commerce). However, s-commerce users experience problems related to its social features that affect s-commerce effectiveness. Therefore, the paper examines the effect of social feature quality (SFQ) determinants on s-commerce from customer perspective by adapting the information systems success model. A total of 220 online survey responses were analyzed by using confirmatory factor analysis and the structural equitation model to test the proposed model. SFQ shows a significant effect on perceived usefulness and customer satisfaction with an s-commerce system, whereas relationship support quality shows a significant effect on perceived usefulness and customer satisfaction with an s-commerce system but not on social support. A significant relationship is also identified among perceived usefulness, customer satisfaction, and net benefits of an s-commerce system.

In this paper the quality aspects of bitrate and loudness in digital broadcasting and webcasting systems are examined. The authors discuss a survey concerning user preferences related with processing and managing audio content. The coding efficiency of a popular audio format is analyzed in the context of storing media. An objective study on a representative group of signal samples, as well as a subjective study of the perceived quality of real-time broadcasted and webcasted radio programs are performed.

This paper deals with the video quality evaluation of mobile video delivery. It analyzes the current trends in video quality evaluation while trying to shift video quality measurement from service-centered to more user-centered. The attention is in defining the causalities among specific video and network parameters, and perceived video quality. As a part of this target, vast subjective video quality survey has been conducted to define these causalities. These causalities were particularly important in designing of our video quality prediction model based on Bayesian network. Proposed solution and prediction results were then experimentally evaluated compared to video quality results of independent subjective video quality assessment.

This paper deals with the analysis of user perceived visual quality for mobile multimedia services. H.264/AVC is selected for low resolution video encoding, and a mobile UMTS network is considered for the access to services. First, from subjective tests results, the combined impact of different service-and radio network-level parameters is inferred. As a result, different cross-layer adaptation alternatives are proposed to maximize the perceived quality level under different service conditions. Afterwards, the backhaul segment is considered taking into account possible congestion effects after the aggregation of multi-user media stream. The proposed dynamic adaptation process determines the best combination of media bitrates in order to optimise the overall quality, by making use of information related to network performance both at the radio and backhaul segments.

This paper describes the experimental deployment of a DASH-based Multi-Sensory Media Delivery System (DASHMS) to support multi-sensorial media-enhanced learning. Multi-sensorial media (mulsemedia) combines multimedia components (video and audio) and emerging technologies targeting other human senses (e.g. touch & haptics, smell and taste). This paper studies learning experience with olfaction and haptic-based mulsemedia when employing DASHMS in a real-life subjective experiment with 40 participants. The tests performed evaluate the user-perceived quality of experience (QoE) and the effects of mulsemedia on user learning.

In this paper, we present the case for using specifically configured protocol stacks geared toward human requirements in the delivery of distributed multimedia. We define the term Quality of Perception (QoP) as representing the user side of the more technical and traditional Quality of Service (QoS). QoP is a term that encompasses not only a user's satisfaction with the quality of multimedia presentations but also his/her ability to analyze, synthesize, and assimilate the informational content of multimedia displays. The Dynamically Reconfigurable Protocol Stacks (DRoPS) project addresses issues of runtime reconfigurable transport systems. The DRoPS architecture supports low cost reconfiguration of individual protocol mechanisms in an attempt to best t

In this paper, we present the design and implementation of FocusVR, a system for effectively and efficiently reducing the power consumption of Virtual Reality (VR) devices by smartly dimming their displays. These devices are becoming increasingly common with large companies such as Facebook (Oculus Rift), and HTC and Valve (Vive), recently releasing high quality VR devices to the consumer market. However, these devices require increasingly higher screen resolutions and refresh rates to be effective, and this in turn, leads to high display power consumption costs. We show how the use of smart dimming techniques, vignettes and color mapping, can significantly reduce the power consumption of VR displays with minimal impact on usability. In particular, we describe the implementation of FocusVR in both Android and the Unity game engine and then present detailed measurement results across 3 different VR devices -- the Gear VR, the DK2, and the Vive. In addition, we present the results of ...

We study a class of Internet-based virtual environments with interactivity and real-time requirements. A relevant representative of this class are Real-Time Online Interactive Applications (ROIA), examples being multi-player online games and high-performance e-learning and training systems. We present how the European project edutain@grid addresses the development of scalable ROIA. After describing the four-layer architecture of edutain@grid, we focus on the Real-Time Framework (RTF) -our novel middleware solution -and report experimental results demonstrating performance and scalability of our approach.

This article describes the architecture and the network communication of a large-scale, networked virtual environment, which is designed to specifically support closely-coupled collaboration in highly interactive scenarios. Its main goals are the maintenance of low latency during user interaction and fast multicasting of messages in order to fulfill consistency requirements. This is achieved by sophisticated message distribution techniques, peer-to-peer connections between C. Anthes, A. Haffegee, P. Heinzlreiter, J. Volkert interacting clients and a global hierarchical communication topology. Scalability is realised through partitioning the virtual world.

Technologies related to communications ensure the transmission of data and in the modern world they are everywhere. The exchange of information during a crisis is naturally of greater interest for study because it is precisely in situations of instability that ensuring connectivity can be crucial and there is often a need for guaranteed channels of communication. There are various connectivity solutions for problems with conventional communication channels. The most popular is the emergency call system 112. Another solution is the BG-Alert system, which is an additional channel to the National Early Warning System or siren system. A third possibility is through the use of the radio frequency spectrum for the construction of terrestrial networks, allowing the provision of electronic communication services after the issuance of a permit to the municipalities in our country. During the measurements in the flooded villages of Bogdan and Slatina in the municipality of Karlovo, as well as during the assembly near the village of Gela, Smolyan region, in these locations were registered various significantly lower indicators of voice transmission via GSM technology or of Internet access via UMTS, LTE and 5G, according to reports from the Communications Regulation Commission (CRC). The reasons are that during disasters and mass events, public communication networks cannot provide the necessary connectivity and this creates the conditions for a crisis and the impossibility of providing a reliable connection to an emergency aid or other specialized public service. The conclusions give us reason to make a recommendation for the development and implementation of alternative channels for connection in case of crises, using the radio frequency spectrum for the construction of local terrestrial networks with a single center for the needs of the municipalities in the Republic of Bulgaria after the issuance of a permit by the CRC.

This study presents a general machine learning framework to estimate the traffic-measurement-level experience rate at given throughput values in the form of a Key Performance Indicator for the cells on base stations across various cities, using busy-hour counter data, and several technical parameters together with the network topology. Relying on feature engineering techniques, scores of additional predictors are proposed to enhance the effects of raw correlated counter values over the corresponding targets, and to represent the underlying interactions among groups of cells within nearby spatial locations effectively. An end-to-end regression modeling is applied on the transformed data, with results presented on unseen cities of varying sizes.

Dynamic adaptive video streaming over HTTP (DASH) plays a crucial role in delivering video across networks. Traditional adaptive bitrate (ABR) algorithms adjust video segment quality based on network conditions and buffer occupancy. However, these algorithms rely on fixed rules, making it challenging to achieve optimal decisions considering the overall context. In this paper, we propose a novel deep-reinforcement-learning-based approach for DASH streaming, with the primary focus of maintaining consistent perceived video quality throughout the streaming session to enhance user experience. Our approach optimizes quality of experience (QoE) by dynamically controlling the quality distance factor between consecutive video segments. We evaluate our approach through a comprehensive simulation model encompassing diverse wireless network environments and various video sequences. We also conduct a comparative analysis with state-of-the-art methods. The experimental results demonstrate signifi...

Burst ratio is a parameter that quantifies packet loss patterns in transmission networks. It has been defined for an end-to-end scenario, therefore burst ratio can be determined only if the characteristics of the whole transmission path are known. In this paper, the burst ratio parameter applicability to cases when the transmission path consists of a series of transmission channels with known packet loss rate and burst ratio values is extended. The paper also presents the results of simulations performed with NS2 software, demonstrating the validity of the burst ratio analysis. Consequently, the research makes it possible to determine the value of the burst ratio parameter in concatenated packet networks, which in turn supports delivering higher quality VoIP services. Keywords—bursty packet loss, E-model, quality of experience, voice over IP.

The paper proposes a No-Reference (NR) quality assessment measurement originally developed for H.264, used for High Efficiency Video Coding (HEVC). In particular, authors present an investigation of NR metrics to objectively estimate the perceptual quality of a set of processed video sequences. The authors take into account typical distortions introduced by the block-based coding approaches like HEVC codec. The underlying processing used for the quality assessment considers the blockiness caused by the boundaries of each coded block and the blurring as a lack of spatial details. The correlation between the NR quality metrics and the well-known and most widely used objective metric, the Video Quality Model (VQM), is performed to validate the quality prediction accuracy based on the provided scores. The Pearson correlation coefficients obtained stand for promising results for different types of videos. Keywords—High Efficiency Video Coding, No-Reference metrics, Quality of Experience,...

Live mobile streaming applications will be among the most important applications in future wireless multimedia systems. Hence, a Quality of Experience (QoE) assessment control mechanism is an essential requirement to assure the video quality level, whiling maximizing profits to service providers and keeping and attracting new customers. This paper studies the requirements to develop a video artifacts assessment mechanism for live mobile streaming applications, introduce a new assessment solution and no-reference QoE metrics. The proposed schemes are evaluated based on psycho-physical experiments faced with artifacts measurements. The results present the benefits of the assessment mechanism in estimating the quality level of live mobile streaming applications.

The paper presents different no reference (NR) objective metrics addressing the most important artefacts for raw (source) video sequences (noise, blur, exposure) and those introduced by compression (blocking, flickering) which can be used for assessing quality of experience. The validity of all metrics was verified under subjective tests.

In recent years, the ubiquity of multimedia services along with the proliferation of mobile devices and the demand for new audio and video applications are changing the lifestyle of users. The multimedia era is allowing users to create, distribute, and access content in ubiquitous way and cost-effectively, while providers explore new ways to increase their revenues. The efficient delivery of real-time multimedia services over emerging diverse and heterogeneous systems is a challenging research goal. The interoperability of applications, transport and network protocols as well as the demand for improved Quality of Experience (QoE) create new challenges and opportunities for further research on novel communication protocols, architectures, and methods to efficiently support current and future multimedia networking systems. In 2010, the Third International Future Multimedia Networking (FMN 2010) workshop was organized in Kraków, Poland and achieved a success attracting numerous submissions from various countries. FMN 2010 produced a high quality peer reviewed technical programme with an acceptance rate at around 24% and addressed important aspects of future multimedia systems with a focus on QoE. Authors of selected papers from FMN 2010 were invited to submit extended versions of their papers in order to be considered for inclusion in this Special issue on Future Multimedia.

The Internet of Things (IoT) is a complete network of networked computer devices, digital and mechanical equipment, and the capacity to send data over the Internet based on machine to machine interaction. It is also known as the Internet of Everything (IoE). The Internet is a packet-switched network, which means that the Quality of Service (QoS) elements (such as packet loss, latency, jitter, and so on) have an influence on the Quality of Experience (QoE) for the Internet of Things services. This research used a subjective evaluation method in order to evaluate the relationship between the quality of service (QoS) measures such as packet loss, latency, and jitter and the quality of experience (QoE) for Internet of Things services. In addition to that, a mapping model from quality of service to quality of experience was suggested. According to the results of this research, there is a close connection between the subjective opinion score and the quality of service (QoS) variables such...

The impact of network performance on user experience is important to know, as it determines the success or failure of a service. Unfortunately, it is very difficult to assess it in real-time on an operational network. Monitoring of network-level performance criteria is easier and more usual. But the problem is then to correlate these network-level Quality of Service (QoS) to the Quality of Experience (QoE) perceived by the users. Efforts have been done in the previous years to map user behaviour to traffic characteristics on the network to QoS. However, being able to successfully relate these traffic characteristics to user satisfaction is not a simple task and still requires further investigations. In this work, we try to associate on one side the correlations between various traffic characteristics measured on an operational network and on the other side the user experience tested on an experimental platform. Our aim is to observe some pronounced trends regarding relationships between both types of results. More precisely, we want to validate how and to what extent the volumes of user sessions represent the level of user satisfaction. Along this way, we need to revise classical relationships between some of the network performance indicators such as loss, download time and throughput in order to strengthen the understanding of this impact on each other and on user satisfaction. This preliminary study is based on the application web.