Abstract This paper presents a study on modelling user free walk mobility in virtual reality (VR) art exhibition. The main objective is to investigate and model users’ mobility sequences during interactions with artwork in VR. We employ a range of machine learning (ML) techniques to define scenes of interest in VR, capturing user mobility patterns. Our approach utilises a long short-term memory (LSTM) model to effectively model and predict users’ future movements in VR environments, particularly in scenarios where clear walking paths and directions are not provided to participants. The DL model demonstrates high accuracy in predicting user movements, enabling a better understanding of audience interactions with the artwork. It opens avenues for developing new VR applications, such as community-based navigation, virtual art guides, and enhanced virtual audience engagement. The results highlight the potential for improved user engagement and effective navigation within virtual environments.

Abstract With the proliferation of consumer virtual reality (VR) headsets and creative tools, content creators are experimenting with new forms of interactive audience experience using immersive media. Understanding user attention and behaviours in virtual environment can greatly inform the creative processes in VR. We developed an abstract VR painting and an experimentation system to study audience art encounters through eye gaze and movement tracking. The data from a user experiment with 35 participants reveal a range of user activity patterns in art exploration. Deep learning models are used to study the connections between the behavioural data and the audience’s background. The work also introduced new integrated methods to visualise user attention for content creators.

There is growing interest in point cloud content due to its central role in the creation and provision of interactive and immersive user experiences for extended reality applications. However, it is impractical to stream uncompressed point cloud sequences over communication networks to end systems because of their high throughput and low latency requirements. Several novel compression methods have been developed for efficient storage and adaptive delivery of point cloud content. However, these methods primarily focus on data metrics and neglect the influence on the actual Quality of Experience (QoE). In this paper, we conduct a user study with 102 participants to analyze the QoE of point cloud sequences and develop a QoE model that can enhance the quality of point cloud content distribution under dynamic network conditions. Our analysis is based on user opinions regarding two representative point cloud sequences, three different frame rates, three viewing distances, and two state-of-the-art point cloud compression libraries, Draco and V-PCC. The results indicate that the proposed models can accurately predict the users' quality perception, with frame rate being the most dominant QoE factor.

The demand for online distribution of high quality and high throughput content has led to non-cooperative competition of network resources between a growing number of media applications. This causes a significant impact on network efficiency, the quality of user experience (QoE), its technical measurements as well as a discrepancy of QoE across user devices. Within a multi-user multi-device environment, automating the measuring and maintenance of perceivable user feedback becomes as critical as achieving the QoE on individual user applications. This paper discusses network- and user-level QoE measurements and prediction over networked multimedia applications and how such techniques can be managed through novel framework designs using programmable networks such as software-defined networks (SDN).

The social experience is an important part of art exhibitions. This demo introduces an eye-gaze based generative art prototype for virtual reality (VR) art exhibitions. Our work extends the visitors' experience from individual art exploration to become content co-creators. The design generates live community artworks based on all visitors' visual interactions with VR paintings. During our VR exhibition at a public gallery, over 100 visitors participated in the new creative process for community-generated artworks.

Adaptive media such as HTTP adaptive streaming (HAS) is becoming a standard tool for online video distribution. The non-cooperative competition of network resources between a growing number of adaptive streaming applications has a significant detrimental impact on the user experience and network efficiency. Existing network infrastructures often prioritise fast packet forwarding and not the quality of the delivered content. Future network management must leverage application and user-level cognitive factors to allocate scarce network resources effectively and intelligently. Our software defined cognitive networking (SDCN) project aims at incorporating new developments in human cognition, media technology and communication networks to ensure the user experience, user-level fairness and network efficiency of online adaptive media.

Virtual Reality (VR) is believed to have a pivotal role in transforming teaching and learning in higher education. The novelty factor and full immersion in a virtual environment can undoubtedly improve students' attention. However, it is still unclear how the use of VR would impact the learning experience and outcomes with respect to knowledge acquisition. We conducted a comparative study on students' performance in a standardised assessment when course content is delivered using VR and conventional lecture slides. Results show that improvement in social interaction and productivity tools in VR are essential for its greater impact in higher education.

The emerging network paradigm of Software Defined Networking (SDN) has been increasingly adopted to improve the Quality of Experiences (QoE) across multiple HTTP adaptive streaming (HAS) instances. However, there is currently a gap between research and reality in this field. QoE models, which offer user-level context to network management processes, are often tested in a simulation environment. Such environments do not consider the effects that network protocols, client programs, and other real world factors may have on the outcomes. Ultimately, this can lead to models not functioning as expected in real networks. On the other hand, setting up an experiment that reflects reality is a time consuming process requiring expert knowledge. This paper shares designs and guidelines of an SDN experimentation framework (SDQ), which offers rapid evaluation of QoE models using real network infrastructures.

The growing popularity and increasing performance of mobile devices is transforming the way in which media can be consumed, from single device playback to orchestrated multi-stream experiences across multiple devices. One of the biggest challenges in realizing such immersive media experience is the dynamic management of synchronicity between associated media streams. This is further complicated by the faceted aspects of user perception and heterogeneity of user devices and networks. This paper introduces a QoE-aware open inter-stream media synchronization framework (IMSync). IMSync employs efficient monitoring and control mechanisms, as well as a bespoke QoE impact model derived from subjective user experiments. Given a current lag, IMSync's aim is to use the impact model to determine a good catch-up strategy that minimizes detrimental impact on QoE. The impact model balances the accumulative impact of re-synchronization processes and the degree of non-synchronicity to ensure the QoE. Experimental results verify the run-time performance of the framework as a foundation for immersive media experience in open N-Screens cloud.

The growing demand for online distribution of high quality and high throughput content is dominating today's Internet infrastructure. This includes both production and user-generated media. Among the myriad of media distribution mechanisms, HTTP adaptive streaming (HAS) is becoming a popular choice for multi-screen and multi-bitrate media services over heterogeneous networks. HAS applications often compete for network resources without any coordination between each other. This leads to quality of experience (QoE) fluctuations on delivered content, and unfairness between end users, while new network protocols, technologies, and architectures, such as software defined networking (SDN), are being developed for the future Internet. The programmability, flexibility, and openness of these emerging developments can greatly assist the distribution of video over the Internet. This is driven by the increasing consumer demands and QoE requirements. This paper introduces a novel user-level fairness model UFair and its hierarchical variant UFairHA, which orchestrate HAS media streams using emerging network architectures and incorporate three fairness metrics (video quality, switching impact, and cost efficiency) to achieve user-level fairness in video distribution. UFairHAhas also been implemented in a purpose-built SDN testbed using open technologies, including OpenFlow. Experimental results demonstrate the performance and feasibility of our design for video distribution over future networks.

The past two decades have seen a shift in the multimedia consumption behaviours from that of collectivism and passivity, to individualism and activity. This paper introduces the architectural design, implementation and user evaluation of a second screen application, which is designed to supersede the traditional user control interface for primary screen interaction. We describe how NSMobile, our second screen application, can be used as a pervasive multimedia platform by integrating user experiences on both the second screen and primary screen. The quantitative and qualitative evaluation of user interactions with interactive TV content also contributes to the future design of second screen applications.

HTTP adaptive streaming (HAS) technology is becoming a popular vehicle for online video delivery. HAS applications often compete for network resources without any coordination between each other in a shared network. This leads to quality of experience (QoE) fluctuations and unfairness between end users. This paper introduces a user-level fairness model (UF) which exploits video quality, switching impact and cost efficiency as the fairness metrics to achieve user-level fairness in resource allocation. Experimental results demonstrate how this model is a foundation to orchestrate the resource consumption of HAS streams.

Living Labs provide a 'human-centric' research approach for the design of new ICT artefacts.In Living Labs users participate over several design stages, providing insights into unexpected ICT use, co-creation and evaluation of new IT solutions.Although this approach is becoming more popular, there is little comparative and reflective work on its practical dynamics, problems and possibilities.In this study, we analyse two 4-year Living Lab projects in Lancaster, UK and Siegen, Germany within the domain of Social TV, and compare experiences.We focus on documenting the purposes, methods and user dynamics that affect the trajectory of such long-term research initiatives, focusing inter alia on the dynamics of researcher/user interaction and the developing issues of trust and managing expectations; emphasizing some often neglected ethical issues and the impact of users' individual characteristics and their role in the community dynamics of Living Labs. RESEARCH HIGHLIGHTS• Comparison and reflection of two 4-year Living Lab studies in domestic environments.• Investigation of dynamics of researcher-user interaction.• Emphasizing ethical issues of long-term Living Lab research projects.

Understanding the dynamics of user interactions and the behaviour of users as they browse for content is vital for advancements in content discovery, service personalisation, and recommendation engines which ultimately improve quality of user experience. In this paper, we analyse how more than 1,100 users browse an online TV service over a period of six months. Through the use of model-based clustering, we identify distinctive groups of users with discernible browsing patterns that vary during the course of the day.

This paper presents a dataset of user statistics collected from a P2P multimedia service infrastructure that delivers both live and on-demand content in high quality to users via different platforms: PC/Mac, and set top boxes. The dataset covers a period of seven months starting from October 2011, exposing a total of over 94k system statistic reports from thousands of user devices at a fine granularity. Such rich data source is made available to fellow researchers to aid in developing better understanding of video delivery mechanisms, user behaviour, and programme popularity evolution.

Wireless Mesh Networks (WMNs) are increasingly deployed to enable thousands of users to share, create, and access live video streaming with different characteristics and content, such as video surveillance and football matches. In this context, there is a need for new mechanisms for assessing the quality level of videos because operators are seeking to control their delivery process and optimize their network resources, while increasing the user’s satisfaction. However, the development of in-service and non-intrusive Quality of Experience assessment schemes for real-time Internet videos with different complexity and motion levels, Group of Picture lengths, and characteristics, remains a significant challenge. To address this issue, this article proposes a non-intrusive parametric real-time video quality estimator, called MultiQoE that correlates wireless networks’ impairments, videos’ characteristics, and users’ perception into a predicted Mean Opinion Score. An instance of MultiQoE was implemented in WMNs and performance evaluation results demonstrate the efficiency and accuracy of MultiQoE in predicting the user’s perception of live video streaming services when compared to subjective, objective, and well-known parametric solutions.

As Wireless Mesh Networks (WMNs) are being increasingly deployed, there is an increasing demand for new quality assessment mechanisms that allow service operators to evaluate and optimize the utilization of network resources, while ensuring a good quality level on multimedia applications as perceived by end-users. However, existing real-time assessment schemes for WMNs are not capable of capturing the actual quality of received multimedia content with regard to user perception. Therefore, it is not possible to assure the user experience of content services. To address this problem, this paper introduces the Hybrid Quality of Experience (HyQoE) Prediction, which is a quality estimator specially designed to assess real-time multimedia applications. HyQoE is designed based on the framework of the widely used Pseudo-Subjective Quality Assessment (PSQA) Tool which exploits Random Neural Network (RNN). Crucial extension work has been implemented to achieve our objectives. A performance evaluation verifies the effectiveness and advantages of HyQoE in predicting users' perception of multimedia content in WMNs over existing subjective and hybrid methods.

Data-sets derived from subjective experiments are often exploited to construct objective quality models using parametric statistics such as MOS and multiple regression. In this paper, using data type and normality tests, we verify that non-normally distributed user opinion scores in nominal or ordinal responses should not be analysed using parametric statistics. The paper introduces a number of non-parametric statistics for valid model building and parameter estimation based on user opinion scores. A set of modelling results are also presented to demonstrate the effectiveness of non-parametric statistics.

Video-on-demand (VoD) is becoming a popular service for commercial content distribution by offering end users the freedom to access recorded programmes. The management of on-demand assets is essential to maximise the efficiency of storage and network utilisation as well as advertisement. This paper introduces our recent efforts in design and implementation of an adaptive VoD archive system in an IPTV infrastructure. The system exploits live statistics on the user behaviours as well as the dynamic popularity of VoD programmes. Using the modelled programme popularity function, the VoD archive is capable of managing the VoD repository by adapting to the most recent user requests. The design has greatly improved the activity of VoD repository and user experience in on-demand services.

As Wireless Mesh Networks (WMNs) have been increasingly deployed, where users can share, create and access videos with different characteristics, the need for new quality estimator mechanisms has become important because operators want to control the quality of video delivery and optimize their network resources, while increasing the user satisfaction. However, the development of in-service Quality of Experience (QoE) estimation schemes for Internet videos (e.g., real-time streaming and gaming) with different complexities, motions, Group of Picture (GoP) sizes and contents remains a significant challenge and is crucial for the success of wireless multimedia systems. To address this challenge, we propose a real-time quality estimator approach, HyQoE, for real-time multimedia applications. The performance evaluation in a WMN scenario demonstrates the high accuracy of HyQoE in estimating the Mean Opinion Score (MOS). Moreover, the results highlight the lack of performance of the well-known objective methods and the Pseudo-Subjective Quality Assessment (PSQA) approach.

The paper introduces NextShareMobile, an integrated second screen application. Design and evaluations are presented.

This paper introduces the recent design and development of a converged IPTV service that has been deployed within a live test-bed (Living Lab) at Lancaster University for thousands of students. High quality audio-visual content is distributed over heterogeneous IP-based content networks, on both set-top box and web-based platforms. Peer-to-peer (P2P) technologies are exploited to provide energy efficient and low-cost delivery for commercial and user-generated content. The infrastructure and functional components are first presented exploring a number of key designs that facilitate the entire eco-system of content ingest, transcoding, P2P tracking, distribution, statistics, end systems, as well as integration of social networking. Due to the dynamic nature of P2P distribution, a quality measurement service with respect to user experience is also essential for the service evaluation and diagnosis. A multimodal QoE measurement framework which evaluates the IPTV services by collaborating measurements with a variety of different aspects is presented. Results of a use case are also described to verify the effectiveness of the measurement framework in exploiting relevant metrics from service components.

Peer-to-Peer (P2P) technologies provide efficient and low-cost delivery for commercial and user-generated content. Although a number of audio-visual content distribution services have been developed using P2P-based mechanisms, it is still a challenge to guarantee satisfactory user experience on high quality video streaming services due to the dynamic nature of P2P distribution. An objective assessment service is the key enabler of service quality monitoring and management. However, this complex task can not be achieved by any individual objective model that only captures a certain aspect of service quality. This paper introduces a multimodal quality evaluation framework that is specifically designed and implemented for the assessment of video streaming services in P2P-based IPTV systems. Results of initial experiments show the effectiveness of this framework.

Network impairments are unpredictable and highly destructive to the perceptual quality of media content in the IPTV content distribution networks. As a result the existing network QoS based assessment methodologies are not adequate for the IPTV QoE assessment which determines the overall acceptability of a service as perceived by the end user. A discrete perceptual impact evaluation quality assessment framework (DEQA) is introduced in this paper. The proposed framework enables a real-time, non-intrusive assessment service by efficiently recognising and assessing the individual IPTV quality violation events in the distribution network. The discrete perceptual impacts to a media session are aggregated for the overall user level quality evaluation. With its deployment scheme, the DEQA framework also facilitates efficient network diagnosis and QoE management. Recent work on realising the functions of the framework is also presented.