As Extended Reality (XR) transitions from individual experiences to multi-user collaborative environments, understanding the dynamics of team connection and cooperation becomes critical. This pilot study investigates the potential of Physiological Synchrony (PS) as an objective measure of team chemistry in Collaborative Virtual Reality (CVR). We conduct a within-subjects study where participant pairs engage in a pizza-making task both in a collaborative and in a competitive scenario. Physiological data, i.e. Galvanic Skin Response (GSR), Photoplethysmogram (PPG), and Interbeat Interval (IBI), are collected and analyzed to quantify synchrony levels and compared to subjective questionnaires. Results confirm that participants perceive significantly higher team chemistry in the collaborative scenario. Objectively, PPG shows a preliminary tendency towards synchrony compared to subjective scores.

eXtended Reality (XR) and holographic telepresence place stringent Quality of Service (QoS) demands on network infrastructure, requiring ultra-low latency, high throughput, and reliable connectivity. Meeting such QoS demands is critical in dynamic, distributed cloud environments, but does not always guarantee a satisfactory user experience. Quality of Experience (QoE) captures the user’s perception of service performance, which may be influenced by factors not fully reflected in systemlevel metrics. Thus, novel orchestration strategies must consider both QoS and QoE. This paper proposes a Reinforcement Learning (RL)-driven approach to edge-cloud orchestration capable of adapting to dynamic network conditions, leveraging a multiobjective reward function, including both QoS and QoE aspects, to guide service placement decisions. Evaluation shows that our RL approach reaches a 21.3% QoE gain over heuristics and 14.7% over balanced strategies, with 100% request acceptance. The results highlight the robustness and scalability of RL-driven orchestration, particularly for latency-sensitive 6G applications. Our findings also reveal the limitations of traditional heuristics under complex objectives and highlight the potential of RL as a transformative tool for intelligent network and service management in next-generation communication systems.

The emergence of the 6 G era demands seamless orchestration across an increasingly heterogeneous and distributed compute continuum-spanning edge, fog, and cloud resources. Moreover, the next generation of the mobile network is poised to redefine the digital landscape by enabling pervasive intelligence, ultra-low latency communication, and extreme heterogeneity across the entire network infrastructure. This transformation introduces unprecedented orchestration challenges due to the dynamic, multi-domain, and resource-constrained nature of emerging workloads such as Generative Artificial Intelligence (GenAI) inference, immersive eXtended Reality (XR), and autonomous systems. To tackle this complexity, we advocate for a Hybrid Hierarchical Digital Twin (DT) architecture that serves as a foundation for intelligent, adaptive, and real-time orchestration in 6 G environments. We present a comprehensive vision for integrating DTs as enablers of intelligent, context-aware, and adaptive orchestration mechanisms that span across multiple domains. The proposed architecture introduces a multi-layered DT hierarchy combining local and global views, enabling scalable coordination and real-time decision-making. We highlight key architectural enhancements required to realize this vision, including inter-twin interoperability and behavioral modeling for QoE estimation. This work aims to guide researchers and practitioners in shaping the foundations of resilient and efficient orchestration frameworks for 6 G systems.

Extended Reality (XR) systems are rapidly shifting from isolated, single-user applications towards collaborative and social multi-user experiences. To evaluate the quality and effectiveness of such interactions, it is therefore required to move beyond traditional individual metrics such as Quality-of-Experience (QoE) or Sense of Presence (SoP). Instead, group-level dynamics such as effective communication, coordination etc. need to be encompassed to assess the shared understanding of goals and procedures. In psychology, this is referred to as a Shared Mental Model (SMM). The strength and congruence of such an SMM are known to be key for effective team collaboration and performance. In an immersive XR setting, though, novel Influence Factors (IFs) emerge that are not considered in a setting of physical co-location. Evaluations on the impact of these novel factors on SMM formation in XR, however, are close to non-existent. Therefore, this work proposes SMMs as a novel evaluation tool for collaborative and social XR experiences. To better understand how to explore this construct, we ran a prototypical experiment based on ITU recommendations in which the influence of asymmetric end-to-end latency is evaluated through a collaborative, two-user block building task. The results show how also in an XR context strong SMM formation can take place even when collaborators have fundamentally different responsibilities and behavior. Moreover, the study confirms previous findings by showing in an XR context that a teams’ SMM strength is positively associated with its performance.

The future of human interaction is virtual. Thus it will require effective collaboration on tasks among users in remote settings. eXtended Reality (XR) is playing a leading role in this transition, offering a realm where virtual collaboration becomes not just possible but essential in situations where physical presence is limited by risk, cost, or complexity. However, while networks are continuously evolving, they can still introduce unexpected impairments that potentially degrade the user perception, i.e., the Quality-of-Experience (QoE), of such Collaborative Virtual Reality (CVR) scenarios. In response to this challenge, this paper presents a demonstrator designed to explicitly showcase the effects of network conditions on CVR. Our platform, centered around a pizza-making game, allows for exploration of the real-time impact of different network parameters, such as packet delay, loss, and throttling on the user engagement and perception in CVR. The framework employs a combination of subjective, objective, and physiological assessments, including the capture of heart rate and skin conductivity, to gain comprehensive insights into user experiences. Our platform not only allows users to directly experience the impact of network impairments on CVR interactions but also provides initial evidence of how such distortions affect both subjective perceptions and objective performance metrics.

EXtended Reality (XR) is already demonstrating its potential beyond the entertainment and gaming industry. One sector clearly benefiting from the advantages of XR is the treatment of stress related mental illnesses by means of Virtual Reality (VR). This is a form of therapy using VR which seeks to help decrease the intensity of the stress responses and anxiety levels due to the various modern-day pressures (e.g., situations, thoughts, or memories which provoke anxiety or fear). While showing promising results, the audiovisual essence of Virtual Reality (VR) can limit the effectiveness of this type of virtual therapy, as the patient’s interaction with the environment is constrained to their visual or at most also their audio senses. As such, including in the immersive therapy tactile therapy could enhance the experience and thus the effectiveness of the therapy. However, this has been largely unexplored. The purpose of this paper is to explore the impact of haptic feedback in reducing anxiety for stress relief treatment. Therefore, we present a haptic-enabled subjective methodology. As use case, we selected the booming case of the Virtual Rage Room (VRR), where participants can vent their rage by (virtually) destroying objects. The results of our study highlight the significantly positive impact of incorporating haptic feedback in mitigating anxiety within this context. Moreover, the analysis reassures the intrinsic value of this treatment as a potent tool for anxiety alleviation.

Dynamic point cloud delivery can provide the required interactivity and realism to six degrees of freedom (6DoF) interactive applications. However, dynamic point cloud rendering imposes stringent requirements (e.g., frames per second (FPS) and quality) that current hardware cannot handle. A possible solution is to convert point cloud into meshes before rendering on the head-mounted display (HMD). However, this conversion can induce degradation in quality perception such as a change in depth, level of detail, or presence of artifacts. This paper, as one of the first, presents an extensive subjective study of the effects of converting point cloud to meshes with different quality representations. In addition, we provide a novel in-session content rating methodology, providing a more accurate assessment as well as avoiding post-study bias. Our study shows that both compression level and observation distance have their influence on subjective perception. However, the degree of influence is heavily entangled with the content and geometry at hand. Furthermore, we also noticed that while end users are clearly aware of quality switches, the influence on their quality perception is limited. As a result, this has the potential to open up possibilities in bringing the adaptive video streaming paradigm to the 6DoF environment.

In recent years, Virtual Reality (VR) has gained attention as a tool for a plethora of applications such as first-aid, firefighting and in the automotive industry. End-user immersion is a key factor in these applications to make the experience representative for its real-life counterpart. By enhancing the traditional audiovisual cues with additional sensory inputs in terms of haptic vibro-tactile and kinesthetic feedback, this immersion can be improved. But are current haptic implementations sufficient to provide the required added value? And how do they compare to other types of VR interaction? In this paper, we present a multi-modal VR training framework able to provide subjective and objective comparisons among three different interaction options: (i) haptic gloves, (ii) traditional VR controllers, and (iii) non-haptic handtracking. We performed a user test where the different interactivity flavours were compared in terms of their influence on both subjective perception and objective performance of the end-user by means of three VR training scenarios. The subjective results show an aversion towards non-haptic handtracking for constrained, cognitively light tasks while a preference towards controllers exist for more cognitively heavy multi-tasking. This is however not reflected in objective results, where differences between interaction methods are far less pronounced.

Point Cloud (PC) streaming has recently attracted research attention as it has the potential to provide six degrees of freedom (6DoF), which is essential for truly immersive media. PCs require high-bandwidth connections, and adaptive streaming is a promising solution to cope with fluctuating bandwidth conditions. Thus, understanding the impact of different factors in adaptive streaming on the Quality of Experience (QoE) becomes fundamental. Mixed Reality (MR) is a novel technology and has recently become popular. However, quality evaluations of PCs in MR environments are still limited to static images. In this paper, we perform a subjective study on four impact factors on the QoE of PC video sequences in MR conditions, including quality switches, viewing distance, and content characteristics. The experimental results show that these factors significantly impact QoE. The QoE decreases if the sequence switches to lower quality and/or is viewed at a shorter distance, and vice versa. Additionally, the end user might not distinguish the quality differences between two quality levels at a specific viewing distance. Regarding content characteristics, objects with lower contrast seem to provide better quality scores.

3D objects are important components in Mixed Reality (MR) environments as they allow users to inspect and interact with them in a six degrees of freedom (6DoF) system. Point clouds (PCs) and meshes are two common 3D object representations that can be compressed to reduce the delivered data at the cost of quality degradation. In addition, as the end users can move around in 6DoF applications, the viewing distance can vary. Quality assessment is necessary to evaluate the impact of the compressed representation and viewing distance on the Quality of Experience (QoE) of end users. This paper presents a demonstrator for subjective quality assessment of dynamic PC and mesh objects under different conditions in MR environments. Our platform allows conducting subjective tests to evaluate various QoE influence factors, including encoding parameters, quality switching, viewing distance, and content characteristics, with configurable settings for these factors.

Point cloud video streaming is a fundamental application of immersive multimedia. In it, objects represented as sets of points are streamed and displayed to remote users. Given the high bandwidth requirements of this content, small changes in the network and/or encoding can affect the users' perceived quality in unexpected manners. To tackle the degradation of the service as fast as possible, real-time Quality of Experience (QoE) assessment is needed. As subjective evaluations are not feasible in real time due to their inherent costs and duration, low-complexity objective quality assessment is a must. Traditional No-Reference (NR) objective metrics at client side are best suited to fulfill the task. However, they lack on accuracy to human perception. In this paper, we present a cluster-based objective NR QoE assessment model for point cloud video. By means of Machine Learning (ML)-based clustering and prediction techniques combined with NR pixel-based features (e.g., blur and noise), the model shows high correlations (up to a 0.977 Pearson Linear Correlation Coefficient (PLCC)) and low Root Mean Squared Error (RMSE) (down to 0.077 on a zero-to-one scale) towards objective benchmarks after evaluation on an adaptive streaming point cloud dataset consisting of sixteen source videos and 453 sequences in total.

Accurate and fast estimations or predictions of the (near) future location of the users of head-mounted devices within the virtual omnidirectional environment open a plethora of opportunities in application domains such as interactive immersive gaming and tele-surgery. Therefore, the past years have seen growing attention to models for viewport prediction in 360֯ environments. Among the approaches, content-agnostic, trajectory-based methods have the potential to provide very fast solutions, as they do not require complex analysis of the videos to provide a prediction. However, accurate trajectory-based viewport prediction is rather difficult due to the intrinsic variability in user behaviour. Furthermore, even when making use of machine learning, current approaches tend to be brute-force and heavily tailored to specific datasets with little comparison to existing benchmarks or publicly available studies. This article presents a generic, content-agnostic viewport prediction method consisting of a window-based approach combined with a preprocessing system to classify behavioural patterns in terms of user clustering and trajectory correlation. Moreover, as the state of the art does not provide a comparative analysis of different approaches, this work contributes to this. Based on the obtained results, a combined prediction model is proposed and evaluated. Our method shows a 36.8% to 53.9% improvement when compared to the static prediction baseline for a prediction horizon of 8 seconds. In addition, a 11.5% to 24.0% improvement to a brute-force machine learning prediction approach is obtained. As such, this work contributes towards the creation of more generic and structured solutions for content-agnostic viewport prediction in terms of data representation, preprocessing and modelling.

Volumetric media streaming will be one of the fundamental technologies to enable near future immersive multimedia experiences. In it, objects represented as sets of points (i.e. point-clouds), are presented to remote users wearing Head-Mounted Displays (HMDs). Due to the stringent bandwidth and latency requirements of such applications, small changes in the network can affect the user in unexpected manners (physical discomfort, lack of concentration, etc.). Therefore, there is a need for assessing the perceived quality of this type of applications in real-time, i.e, the Quality of Experience (QoE). Given that subjective evaluations are not feasible for (near) real-time applications, objectively measuring this quality will be a must. While traditional objective metrics could potentially be used to fulfill the task, it is still unclear how accurate they are to assess volumetric media. To this end, this paper presents a thorough correlation analysis of both Full Reference (FR) and No Reference (NR) objective metrics to subjective Mean Opinion Scores (MOS) for different volumetric streaming scenarios. To enhance the accuracy, multiple Region-Of-Interest (ROI) selection and weighting procedures have been applied and their influence on the results have been investigated. Our results show that the classical video quality metric Video Multimethod Assessment Fusion (VMAF) is well-suited as an objective benchmark for volumetric media streaming in terms of correlation to subjective scores, while a combination of NR features could provide a suitable real-time assessment. Finally, ROI selection proves to widen the range of objective metrics, which is an important issue to apply traditional objective metrics to volumetric media.

Augmented Reality (AR) and Virtual Reality (VR) multimodal systems are the latest trend within the field of multimedia. As they emulate the senses by means of omnidirectional visuals, 360° sound, motion tracking and touch simulation, they are able to create a strong feeling of presence and interaction with the virtual environment. These experiences can be applied for virtual training (Industry 4.0), tele-surgery (healthcare) or remote learning (education). However, given the strong time and task sensitiveness of these applications, it is of great importance to sustain the end-user quality, i.e. the Quality-of-Experience (QoE), at all times. Lack of synchronization and quality degradation need to be reduced to a minimum to avoid feelings of cybersickness or loss of immersiveness and concentration. This means that there is a need to shift the quality management from system-centered performance metrics towards a more human, QoE-centered approach. However, this requires for novel techniques in the three areas of the QoE-management loop (monitoring, modelling and control). This position paper identifies open areas of research to fully enable human-centric driven management of immersive multimedia. To this extent, four main dimensions are put forward: (1) Task and well-being driven subjective assessment; (2) Real-time QoE modelling; (3) Accurate viewport prediction; (4) Machine Learning (ML)-based quality optimization and content recreation. This paper discusses the state-of-the-art, and provides with possible solutions to tackle the open challenges.