Extended Reality (XR) is rapidly transitioning from individual experiences towards socially immersive environments. This shift towards Social XR introduces new quality demands that existing XR standardisation efforts centred around Quality of Experience (QoE) have only recently started to explicitly acknowledge, yet they merit more systematic attention. This position paper examines the treatment of exhibited social aspects in QoE-related standardisation efforts across three major Standards Developing Organisations (SDOs) in the telecom domain: 3GPP, ITU-T, and ETSI. Through comparative analysis, we identify how constructs such as presence, user representation, interaction quality, and use cases are addressed, and where shortcomings persist. While ITU-T leads in conceptualising social aspects of QoE, efforts across the SDO landscape remain fragmented and are still largely centred around system-level technical performance. We argue that future QoE-centric standardisation for XR should explicitly model and prioritise social interaction as a core concern. As multi-user immersive experiences become the norm, standardisation should address the experiential dynamics of Social XR alongside system-level metrics. We contend that recognising social factors as core elements, and not by-products, of QoE modelling is essential to align future QoE standards with the practicalities of Social XR.

A mobile robotic platform is typically equipped with motors to enable movement, various sensors to perceive the environment, computing systems, and communication modules that allow remote control (teleoperation) or integration into larger systems. Teleoperation of such platforms often relies on real-time visual feedback, typically provided through camera feeds. Utilizing immersive interfaces, particularly those leveraging virtual reality (VR), offers a heightened sense of presence and spatial awareness, with the potential to enhance both user experience and task performance. We describe the design of a VR-based interface for intuitive teleoperation of a mobile robotic platform via a private 5G network infrastructure, enabling real-time navigation and manipulation by utilizing a synchronized virtual replica of the robot and environment. We evaluate teleoperation performance in terms of user experience and navigation task completion time for different teleoperation view modes (first person view, VR view with camera feed, VR view without camera feed), and robot control methods (manual vs. point-and-click).

Recently, the Entertainment Software Association announced the Accessible Games Initiative (AGI), a project aimed at informing consumers about the accessibility features of commercial digital games. While AGI promotes broadly applicable tags, it does not provide specific or detailed information about Extended Reality (XR) applications, which often present greater challenges related to comfort, accessibility, and safety (CAS) compared to less immersive platforms. In this paper, we describe a prototype web-based XR app marketplace that extends existing tags and guidelines to provide XR users with transparent information regarding CAS features, aiming to facilitate access to content that aligns with their specific needs and preferences. We present the findings of a preliminary user study, with a small participant sample from a contextually relevant population, in preparation for a future study with our target population. The study explored the clarity and specificity of the proposed tags, as well as the user experience and usability of the prototype website.

Collaborative training applications leveraging Augmented Reality (AR) are increasingly being used across various domains to provide immersive and interactive experiences. In this paper, we report on a user study designed to evaluate and compare user experiences across heterogeneous AR platforms used simultaneously within a collaborative co-located cybersecurity training application called Login PARtners. 36 participants were organised into pairs, with one participant using a Head-Mounted Display (HMD), HoloLens 2, and another a Hand-Held Device (HHD), an Android mobile phone, representing different affordability and availability spectra. Through a set of questionnaires, we investigated users’ perception of the virtual environment’s visual aspects, virtual object interaction and gestures, as well as collaboration, across both devices. While results showed some significant differences between HMD and HHD users, namely with respect to perceived virtual object three-dimensionality and interaction intuitiveness, both participant groups generally found the collaborative experience to be successful.

Quality of Experience (QoE) for mobile Web browsing is a complex and multidimensional concept. So far, the research community has addressed the influence of QoE perceptual dimensions on QoE and the impact of various influence factors on QoE perceptual dimensions for mobile Web browsing. In this study, we recognize the gap and need to analyze and gain a deeper understanding of the relationship between multiple influence factors and specific constructs of QoE perceptual dimensions. Utilizing a previously collected dataset, our goal is to fill the research gap in terms of investigating the impact of specific influence factors (loading time, aesthetics, number of taps, information quality) on various constructs comprising the user perception of usability, aesthetics, and information quality in the context of mobile Web browsing. A total of 14 research questions are raised: 7 addressing constructs comprising perceived usability (simplicity, effectiveness, quickness, efficiency, comfort, ease of use, organization), 3 addressing constructs comprising perceived aesthetics (perception of mobile Web site aesthetics, pleasance, clarity, systematicness), and 4 addressing constructs comprising perceived information quality (timeliness, clarity, conciseness, structuralism/completeness/spelling-correctness). Results of posed research questions provide indications on the strength and direction of the impact of considered QoE influence factors on the perception of studied constructs. This study contributes to the research community by being the first to address the impact of chosen QoE influence factors on individual constructs comprising QoE perceptual dimensions in the domain of mobile Web browsing, thus extending the knowledge on interplays and relations in this area. Furthermore, it contributes by revealing numerous new paradigms in the context of usability, aesthetics, and quality of information, such as: (i) beautiful and easy to reach is more simple, effective, efficient, and comfortable, (ii) easy to reach is more pleasant and clear, (iii) informative is more pleasant and clear, (iv) beautifully presented information has higher perceived quality, etc. Reported findings can be utilized by various stakeholders (mobile Web site designers, content providers, device designers, network providers) for indirect management of QoE.

While the topic of investigating user experience with immersive services, such as Social Virtual Reality (VR), is starting to gain traction in the research community, the unique case of multiplayer VR games requires a more specific approach. Attempts to investigate user experiences with this complex, multidimensional service are hindered by the absence of specific standards and guidelines going beyond what we know about non-immersive gaming. In this article, we present the results of a user study (N = 32) exploring participants’ experience of playing two competitive VR games of different genres (shooter, sports game), as we focus on three distinct facets of multiplayer VR gaming—network, interpersonal competitiveness, and social interaction. Furthermore, approaching the issue from the perspective of Quality of Experience researchers looking to conduct future user studies on the topic, we also present our findings as a way to shed light on factors that need further consideration, especially in the context of participant recruitment and methodology design, such as the choice of access network to be used for testing, and the issue of matchmaking study participants based on skill and prior relationship.

While factors such as discomfort due to ergonomic design, cybersickness, and accessibility have been studied, industry platforms offering a wide range of VR apps have been found to report only limited 'comfort ratings' (e.g., Meta), age ratings confirming appropriateness of content (e.g., Steam, Microsoft), or seizure warnings (e.g., Microsoft). In this position paper, we argue that such ratings are insufficient in terms of appropriately warning users and enabling them to make informed decisions on which apps to use. We therefore call for a joint industry and academic initiative toward proposing a standardized method of rating the comfort, accessibility, and safety of publicly available VR applications, thus aiming to address the diverse capabilities, requirements, and preferences of VR users.

Abstract Locomotion techniques are an important part of the virtual reality (VR) experience, as they enable a means of movement within simulated computer graphics. Studies have shown that locomotion techniques cause unpleasant effects related to cybersickness. In this article, we report on the results of a user study (N = 29) aimed to investigate and compare three types of locomotion techniques in VR in terms of their impact on cybersickness. The different locomotion techniques are tested in two different contrast scene settings, daytime and nighttime, to further explore the potential impact of scene contrast adjustments. For the evaluation of cybersickness, we used a questionnaire to obtain subjective ratings, and heart rate monitoring as an objective metric. Results show that a linear movement locomotion technique provokes the highest level of cybersickness, and that women have a higher heart rate as compared to men when navigating and interacting in a VR scene. Regarding the influence of scene contrast, results showed that scenes with daylight were better suited to participants in almost all tested scenarios. In addition to reported findings related to locomotion techniques and the impact on cybersickness, we highlight that a key contribution is the utilized test methodology.

With advances in network capabilities, the gaming industry is increasingly turning towards offering "gaming on demand" solutions, with cloud gaming services such as Sony PlayStation Now, Google Stadia, and NVIDIA GeForce NOW expanding their market offerings. Similar to adaptive video streaming services, cloud gaming services typically adapt the quality of game streams (e.g., bitrate, resolution, frame rate) in accordance with current network conditions. To select the most appropriate video encoding parameters given certain conditions, it is important to understand their impact on Quality of Experience (QoE). On the other hand, network operators are interested in understanding the relationships between parameters measurable in the network and cloud gaming QoE, to be able to invoke QoE-aware network management mechanisms. To encourage developments in these areas, comprehensive datasets are crucial, including both network and application layer data. This paper presents CGD, a dataset consisting of 600 game streaming sessions corresponding to 10 games of different genres being played and streamed using the following encoding parameters: bitrate (5, 10, 20 Mbps), resolution (720p, 1080p), and frame rate (30, 60 fps). For every combination repeated five times for each game, the dataset includes: 1) gameplay video recordings, 2) network traffic traces, 3) user input logs (mouse and keyboard), and 4) streaming performance logs.

With significant growth in video streaming services, coupled with widespread use of traffic encryption, network operators are faced with the challenge of monitoring Key Performance Indicators (KPI) needed to detect quality impairments and drive Quality of Experience (QoE) management mechanisms. Though literature suggests that QoE/KPIs can be inferred from encrypted network traffic using machine learning (ML) methods, most studies published thus far fail to account for frequent viewer interactions, such as pauses, seeking forward/backward, or video abandonment. Such playback-related interactions inherently impact traffic patterns used as input for ML–based KPI estimation models. In this paper, we investigate to what extent network operators can monitor application-layer KPIs considering realistic user interactions, focusing on the use-case of a popular streaming platform with videos streamed to mobile devices. We first investigate the impact of user interactions (pause, seek forward, and abandonment) on the performance of both session-based and real-time KPI classification models trained on datasets that do not contain interactions. Secondly, we systematically evaluate the performance of KPI estimation models trained on datasets including specific sets of interactions to determine which types of interactions need to be included in the model training procedure in order to be applicable for realistic streaming sessions.

The technology of Unmanned Aerial Vehicles (UAVs), or simply drones, has gained much attention over the past two decades, with a rapid decrease in cost and increase in potential applications such as surveillance, journalism, agriculture, film-making, entertainment, etc. With the growth in the popularity of First-person view (FPV) drone piloting comes the challenge of transmitting control information and streaming video at very low latencies to enable proper real-time control. Due to strict latency requirements, FPV technology stagnated, with analog video transmission currently representing the most common implementation. In this paper, we present our preliminary research directed towards developing a digital alternative to this technology. We report on a user study designed to assess the Quality of Experience (QoE) of an FPV system coupled with a cloud-based drone flight simulator, with the focus being on video quality in ideal network conditions. Obtained results show that video encoding parameters have a significant impact on user perceived QoE. Furthermore, our findings highlight the relevance of simulator sickness in this kind of system.

The capabilities of commercial virtual reality (VR) systems can be utilized to implement a wide spectrum of interaction and locomotion methods. However, immersion in VR is often accompanied by undesirable physical sensations that undermine the VR gaming experience. Presenting the results of a user study (N=20) comparing the physiological and cognitive aftereffects of three different VR games with game mechanics centered around natural interaction, this paper attempts to shed light on the impact of different content factors (e.g., game genre, visual environment, interaction method) on user comfort. While moving away from cybersickness as the primary measure of VR-induced discomfort through investigating a broader set of physical symptoms, such as device-related discomfort, muscle fatigue, and pain, we also include a discussion on the complexities of measuring cybersickness for physically demanding games. Further focusing on cognitive performance, we discuss preliminary findings regarding different effects of common VR game interactions (pick and place, slicing, and shooting) on reaction time.

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.

Solutions relying on machine learning (ML) models that address the challenge of in-network QoE estimation for HTTP adaptive video streaming often neglect user behavior and its impact on performance estimation. End user playback-related interactions impact network traffic characteristics, thus having a (predominantly negative) impact on the performance of models that estimate Key Performance Indicators (KPIs) from encrypted traffic. The biggest challenge in incorporating user interactions when training and testing ML models lies in the wide range of different potential interactions, multiple interaction occurrences, various combinations of different interactions, and different time points of execution spanning across a video streaming session. With the aim of training models applicable for deployment in real networks, but also in an effort to optimize the overall process of model training, we systematically investigate the relationship between classification accuracy of models trained on data with and without certain user interactions. Our results for YouTube videos, played using the native YouTube app on a mobile device under emulated broadband network conditions, show that the impact of interactions on model performance highly depends on the target KPI being classified. In certain cases, the model training process may be simplified by reducing the need to consider a wide range of interaction scenarios.

In this paper we present the results of a user study focused on evaluating the overall usability of a wearable video-communication system aimed at facilitating hands-free communication between a field technician and a remote expert. An asymmetrical cooperative electronics-based bomb defusal game was used to examine the subject's ability to perform a manual technical task with the aid of a head-mounted-display (compared to only verbal communication) in terms of both subjective (perceived usefulness for the given task) and objective (time to complete task, number of errors) metrics. We analyze different dimensions of usability (overall system usefulness, information quality, interface quality, and device comfort) as we discuss the impact of human factors (i.e., gender, wearing Rx eye-glasses) on user experience. While system use has been shown to improve both subjective and objective metrics in some cases, results appear to be highly dependant on the task itself. Furthermore, our findings highlight the importance of exploring different ways in which ergonomic issues present in commercial smart glasses may decrease perceived usability in certain demographics.

We address the challenge faced by service providers in monitoring Quality of Experience (QoE) related metrics for WebRTC-based audiovisual communication services. By extracting features from various application-layer performance statistics, we explore the potential of using machine learning (ML) models to estimate perceivable quality impairments and to identify root causes. We argue that such performance-related data can be valuable and informative from a QoE assessment point of view, by allowing to identify the party/parties in a call that is/are experiencing quality impairments, and to trace the origins and causes of the problem. The paper includes case studies of multi-party videoconferencing that are established in a laboratory environment and exposed to various network disturbances and CPU limitations. Our results show that perceivable quality impairments in terms of video blockiness and audio distortions may be estimated with a high level of accuracy, thus proving the potential of exploiting ML models for automated QoE-driven monitoring and estimation of WebRTC performance.

Recent research efforts have addressed the challenge of estimating HTTP adaptive video streaming Quality of Experience (QoE) and Key Performance Indicators (KPIs) from a network provider perspective, commonly relying on machine learning models and the analysis of features extracted solely from encrypted network traffic. This challenge is further complicated in light of realistic end user playback-related interactions, such as video skipping, pausing, and seeking. Given that user interactions impact traffic characteristics, such scenarios need to be considered when training QoE/KPI estimation models. We train models on datasets with and without user interactions (focusing on YouTube as a case study), with the aim to investigate the impact of user interaction on classification accuracy. Results motivate the need to systematically include data corresponding to various interaction scenarios when training QoE/KPI classification models that would be applicable in real-world scenarios.

In the context of subjective user studies conducted to derive relationships between influence factors and QoE, user diversity leads to distributions of user scores for test conditions. Such models are commonly exploited by service/network providers to derive various QoE metrics in their system, such as expected QoE, or the percentage of users rating above a certain threshold. The question arises as to how to combine a) user rating distributions obtained from subjective studies, and b) system performance condition distributions, so as to obtain the actual observed QoE distribution in the system? Moreover, how can various QoE metrics of interest in the system be derived? We prove a fundamental relationship showing that the expected system QoE is equal to the expected Mean Opinion Score (MOS) in the system. While subjective user studies commonly report only QoS-to-MOS mapping functions, we show that to derive additional QoE metrics in the system, it is necessary to use corresponding QoS-to-QoE metric mapping functions (beyond only QoS-to-MOS) as derived from user rating distributions in subjective studies. The results of the paper provide important insights for deriving QoE metrics from a systems perspective.

With the widespread use of encryption in Over the Top (OTT) traffic, Internet Service Providers (ISPs) for the most part lack insights into application performance, as well as into Quality of Experience (QoE) perceived by end users. Addressing challenges related to encryption, ISPs are looking into machine learning (ML) based solutions that can detect application performance solely from statistical properties of the traffic. On the other hand, OTT service providers are not willing to share service performance and content-related information with ISPs. While related work on OTT-ISP collaboration scenarios has addressed architectural aspects, business models, and to a certain extent incentives for sharing data, the focus of this paper is on the exchanged data itself. We investigate to what extent the performance of in-network ML-based QoE estimation models for HTTP adaptive video streaming could be improved with the availability of certain context data provided by OTT providers. We motivate OTT-ISP collaboration through more accurate in-network QoE monitoring and potential improvement of user experience, which is of interest to both sides.

In this paper we present the results of two user studies aimed at exploring the effects of network latency on user experience in a First Person Shooter Virtual Reality multiplayer game. In both studies we observed the following metrics: Quality of Experience (QoE), willingness to continue playing in given network conditions, and the final outcome of the game (survival/death). After conducting the first study, we concluded that test methodology and contextual factors (e.g., social context, level of difficulty, weapon choice) may have had a strong influence on perceived QoE, subsequently causing inconclusive results. Therefore, we aimed to mitigate those factors through different methodology choices in the second study, which led to more conclusive, statistically significant results, indicating that user experience for the chosen game begins to suffer in cases of latency greater than approx. 100 ms (round trip time between client and server). In addition to the analysis of acquired results, we discuss the significance of methodology and context when conducting studies exploring gaming QoE in multiplayer environments.

QoE has received much attention over the past years and has become a prominent issue for delivering services and applications. A significant amount of research has been devoted to understanding, measuring, and modeling QoE for a variety of media services. In this position paper we provide an overview of state-of-the-art findings and discuss emerging concepts and the challenges they raise with respect to managing QoE for networked media services. We address the implications of this evolution in our understanding of QoE in terms of new approaches in QoE modeling that are necessary for achieving a more comprehensive QoE management paradigm.

Over the last few years, different client-side QoE monitoring apps have been developed that benchmark the performance of popular video streaming services. Such tools also provide the means for collecting ground truth data when developing models to estimate or classify QoE and various KPls from encrypted network traffic. We present a client-side YouTube QoE monitoring tool named ViQMon, which extracts YouTube performance data from the official app's Stats for Nerds window, and is applicable on various devices and platforms (Android, iOS). We compare ViQMon to approaches relying on YouTube's APls, and show relevant differences in buffering and application behavior in cases when videos are embedded and when videos are played in the official YouTube app. We further use ViQMon together with the collection of network measurements in both a laboratory and commercial mobile network to collect a large dataset of almost 500 YouTube videos streamed under different network conditions. The dataset is used to build machine learning based models for estimating QoE and various application-layer KPls solely from IP-level network traffic features. As such, the approach is applicable in the context of both TLS and QUIC traffic. The paper further compares and analyses the performance of the built models.

This paper presents the results of a subjective study aimed at assessing different locomotion (may also be referred to as navigation) techniques in virtual reality environments. We implemented a simple virtual environment in which participants were asked to assess the following four techniques using the HTC Vive platform: teleportation, controller movement, controller movement with tunneling vision, and human joystick. A total of 29 participants took part in the study and rated perceived level of immersion, physical discomfort, and overall Quality of Experience for all tested locomotion techniques. Moreover, participants indicated whether they needed a break after a specific test due to physical discomfort. The results indicate that teleportation yields overall highest QoE and lowest discomfort.

editorial Free Access Share on Guest Editorial: Special Issue on “QoE Management for Multimedia Services” Editors: Lea Skorin-Kapov University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia University of Zagreb, Faculty of Electrical Engineering and Computing, CroatiaView Profile , Martín Varela callstats.io, Finland callstats.io, FinlandView Profile , Tobias Hoßfeld Chair of Communication Networks, Computer Science, University of Würzburg, Germany Chair of Communication Networks, Computer Science, University of Würzburg, GermanyView Profile , Kuan-Ta Chen Academia Sinica, Taiwan Academia Sinica, TaiwanView Profile Authors Info & Claims ACM Transactions on Multimedia Computing, Communications, and ApplicationsVolume 14Issue 2sApril 2018 Article No.: 28pp 1–3https://doi.org/10.1145/3192332Published:25 April 2018Publication History 5citation269DownloadsMetricsTotal Citations5Total Downloads269Last 12 Months29Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Quality of Experience (QoE) has received much attention over the past years and has become a prominent issue for delivering services and applications. A significant amount of research has been devoted to understanding, measuring, and modelling QoE for a variety of media services. The next logical step is to actively exploit that accumulated knowledge to improve and manage the quality of multimedia services, while at the same time ensuring efficient and cost-effective network operations. Moreover, with many different players involved in the end-to-end service delivery chain, identifying the root causes of QoE impairments and finding effective solutions for meeting the end users’ requirements and expectations in terms of service quality is a challenging and complex problem. In this article, we survey state-of-the-art findings and present emerging concepts and challenges related to managing QoE for networked multimedia services. Going beyond a number of previously published survey articles addressing the topic of QoE management, we address QoE management in the context of ongoing developments, such as the move to softwarized networks, the exploitation of big data analytics and machine learning, and the steady rise of new and immersive services (e.g., augmented and virtual reality). We address the implications of such paradigm shifts in terms of new approaches in QoE modeling and the need for novel QoE monitoring and management infrastructures.

Cloud gaming has been recognized as a promising shift in the online game industry, with the aim of implementing the “on demand” service concept that has achieved market success in other areas of digital entertainment such as movies and TV shows. The concepts of cloud computing are leveraged to render the game scene as a video stream that is then delivered to players in real-time. The main advantage of this approach is the capability of delivering high-quality graphics games to any type of end user device; however, at the cost of high bandwidth consumption and strict latency requirements. A key challenge faced by cloud game providers lies in configuring the video encoding parameters so as to maximize player Quality of Experience (QoE) while meeting bandwidth availability constraints. In this article, we tackle one aspect of this problem by addressing the following research question: Is it possible to improve service adaptation based on information about the characteristics of the game being streamed? To answer this question, two main challenges need to be addressed: the need for different QoE-driven video encoding (re-)configuration strategies for different categories of games, and how to determine a relevant game categorization to be used for assigning appropriate configuration strategies. We investigate these problems by conducting two subjective laboratory studies with a total of 80 players and three different games. Results indicate that different strategies should likely be applied for different types of games, and show that existing game classifications are not necessarily suitable for differentiating game types in this context. We thus further analyze objective video metrics of collected game play video traces as well as player actions per minute and use this as input data for clustering of games into two clusters. Subjective results verify that different video encoding configuration strategies may be applied to games belonging to different clusters.

Managing QoE is one of the most interesting direct applications of workable QoE models. Indeed, being able to predict how users perceive the quality of a service allows the service provider(s) to optimize its delivery, based on several possible criteria. It has been argued, however, that the MOS is ill-suited for this type of application, and that different measures — e.g., rating distributions or quantiles — are better suited for the task. In this paper we build on these ideas by adding the notions of QoE fairness (as opposed to QoS fairness) and user diversity, and discuss how the choice of measures used, the importance of fairness, and how the variations between users can affect the optimal QoE management choices for service providers.

Cloud gaming has been recognized as a promising shift in the online game industry, with the aim being to deliver high-quality graphics games to any type of end user device. The concepts of cloud computing are leveraged to render the game scene as a video stream which is then delivered to players in real-time. Given high bandwidth and strict latency requirements, a key challenge faced by cloud game providers lies in configuring the video encoding parameters so as to maximize player Quality of Experience (QoE) while meeting bandwidth availability constraints. In this paper we address this challenge by conducting a subjective laboratory study involving 52 players and two different games aimed at identifying QoE-driven video encoding adaptation strategies. Empirical results are used to derive analytical QoE estimation models as functions of bitrate and framerate, while also taking into account game type and player skill. Results have shown that under certain identified bandwidth conditions, reductions of framerate lead to QoE improvements due to improved graphics quality. Given that results indicate that different QoE-driven video adaptation policies should likely be applied for different types of games, we further report on objective video metrics that may be used to classify games for the purpose of choosing an appropriate and QoE-driven video codec configuration strategy.

Cloud gaming represents a highly interactive service whereby game logic is rendered in the cloud and streamed as a video to end devices. While benefits include the ability to stream high-quality graphics games to practically any end user device, drawbacks include high bandwidth requirements and very low latency. Consequently, a challenge faced by cloud gaming service providers is the design of algorithms for adapting video streaming parameters to meet the end user system and network resource constraints. In this paper, we conduct an analysis of the commercial NVIDIA GeForce NOW game streaming platform adaptation mechanisms in light of variable network conditions. We further conduct an empirical user study involving the GeForce NOW platform to assess player Quality of Experience when such adaptation mechanisms are employed. The results provide insight into limitations of the currently deployed mechanisms, as well as aim to provide input for the proposal of designing future video encoding adaptation strategies.

Over the last decade or so, significant research has focused on defining Quality of Experience (QoE) of Multimedia Systems and identifying the key factors that collectively determine it. Some consensus thus exists as to the role of System Factors, Human Factors and Context Factors. In this paper, the notion of context is broadened to include information gleaned from simultaneous out-of-band channels, such as social network trend analytics, that can be used if interpreted in a timely manner, to help further optimise QoE. A case study involving simulation of HTTP adaptive streaming (HAS) and load balancing in a content distribution network (CDN) in a flash crowd scenario is presented with encouraging results.

A novel class of mobile Internet of Things applications falls under the category of mobile crowdsensing, whereby large amounts of sensed data are collected and shared by mobile sensing and computing devices for the purposes of observing phenomena of common interest. Challenges arise with respect to

The problem of dynamic Quality of Service (QoS) management for multimedia services has been addressed from two complementary, yet interrelated directions; one mainly dealing with designing adaptive or “elastic” services, and the other, dealing with “smarter” resource allocation. We adopt this approach in this paper, by performing a graceful quality degradation and the corresponding resource (re)allocation for multiple multimedia sessions in case of decreasing network resources. We introduce a mathematical model which optimizes overall system utility by taking user satisfaction and operator profit into account.

Providing seamless and acceptable quality of service (QoS) support for multimedia services in heterogeneous environments implies an adaptive QoS system, capable not only of establishing end-to-end QoS support at session setup, but also of reacting dynamically to changes occurring during the session. We propose a model for the dynamic negotiation and adaptation of QoS parameters for networked virtual reality (NVR) services, considering such services to be among the most complex type of multimedia. In addition, we propose generic client and service profiles used in the negotiation process. While the model is independent of the particular service and network scenario, certain elements are influenced by QoS requirements specified for the Universal Mobile Telecommunications System (UMTS). The model is implemented in a laboratory testbed and a case study is described to verify functionality.

This paper presents a model for an adaptive virtual reality (VR) service. The service adaptation, as described by the model, is performed by matching the parameters of-and possibly transcoding-the VR service in order to achieve the "best possible quality", given the limitations of the terminal and access network, and taking into account individual user preferences. The matching process is based on two sets of parameters, called "profiles": the VR service profile, which describes the VR service, and the client capabilities and preferences profile, which describes the client. Both profiles use extensible markup language for notation, and are thus in line with the existing W3C standards. The model is independent of the particular VR service and network scenario, and it is suitable for heterogeneous environments such as the Internet and 3G wireless networks. A case study involving two prototype VR services demonstrates the applicability of the proposed approach.