Streaming and User Behaviour in Omnidirectional Videos

Omnidirectional videos (ODVs) have gone beyond the passive paradigm of traditional video, offering higher degrees of immersion and interaction. The revolutionary novelty of this technology is the possibility for users to interact with the surrounding environment, and to feel a sense of engagement and presence in a virtual space. Users are clearly the main driving force of immersive applications and consequentially the services need to be properly tailored to them. In this context, this chapter highlights the importance of the new role of users in ODV streaming applications, and thus the need for understanding their behaviour while navigating within ODVs. A comprehensive overview of the research efforts aimed at advancing ODV streaming systems is also presented. In particular, the state-of-the-art solutions under examination in this chapter are distinguished in terms of system-centric and user-centric streaming approaches: the former approach comes from a quite straightforward extension of well-established solutions for the 2D video pipeline while the latter one takes the benefit of understanding users’ behaviour and enable more personalised ODV streaming

Do Users Behave Similarly in VR? Investigation of the User Influence on the System Design

With the overarching goal of developing user-centric Virtual Reality (VR) systems, a new wave of studies focused on understanding how users interact in VR environments has recently emerged. Despite the intense efforts, however, current literature still does not provide the right framework to fully interpret and predict users’ trajectories while navigating in VR scenes. This work advances the state-of-the-art on both the study of users’ behaviour in VR and the user-centric system design. In more detail, we complement current datasets by presenting a publicly available dataset that provides navigation trajectories acquired for heterogeneous omnidirectional videos and different viewing platforms—namely, head-mounted display, tablet, and laptop. We then present an exhaustive analysis on the collected data to better understand navigation in VR across users, content, and, for the first time, across viewing platforms. The novelty lies in the user-affinity metric, proposed in this work to investigate users’ similarities when navigating within the content. The analysis reveals useful insights on the effect of device and content on the navigation, which could be precious considerations from the system design perspective. As a case study of the importance of studying users’ behaviour when designing VR systems, we finally propose a user-centric server optimisation. We formulate an integer linear program that seeks the best stored set of omnidirectional content that minimises encoding and storage cost while maximising the user’s experience. This is posed while taking into account network dynamics, type of video content, and also user population interactivity. Experimental results prove that our solution outperforms common company recommendations in terms of experienced quality but also in terms of encoding and storage, achieving a savings up to 70%. More importantly, we highlight a strong correlation between the storage cost and the user-affinity metric, showing the impact of the latter in the system architecture design

Human-centric quality management of immersive multimedia applications

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 omni-directional visuals, 360 degrees 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

Understanding user interactivity for the next-generation immersive communication: design, optimisation, and behavioural analysis

Recent technological advances have opened the gate to a novel way to communicate remotely still feeling connected. In these immersive communications, humans are at the centre of virtual or augmented reality with a full sense of immersion and the possibility to interact with the new environment as well as other humans virtually present. These next-generation communication systems hide a huge potential that can invest in major economic sectors. However, they also posed many new technical challenges, mainly due to the new role of the final user: from merely passive to fully active in requesting and interacting with the content. Thus, we need to go beyond the traditional quality of experience research and develop user-centric solutions, in which the whole multimedia experience is tailored to the final interactive user. With this goal in mind, a better understanding of how people interact with immersive content is needed and it is the focus of this thesis. In this thesis, we study the behaviour of interactive users in immersive experiences and its impact on the next-generation multimedia systems. The thesis covers a deep literature review on immersive services and user centric solutions, before develop- ing three main research strands. First, we implement novel tools for behavioural analysis of users navigating in a 3-DoF Virtual Reality (VR) system. In detail, we study behavioural similarities among users by proposing a novel clustering algorithm. We also introduce information-theoretic metrics for quantifying similarities for the same viewer across contents. As second direction, we show the impact and advantages of taking into account user behaviour in immersive systems. Specifically, we formulate optimal user centric solutions i) from a server-side perspective and ii) a navigation aware adaptation logic for VR streaming platforms. We conclude by exploiting the aforementioned behavioural studies towards a more in- interactive immersive technology: a 6-DoF VR. Overall in this thesis, experimental results based on real navigation trajectories show key advantages of understanding any hidden patterns of user interactivity to be eventually exploited in engineering user centric solutions for immersive systems

Deep Learning Approach for Seamless Navigation in Multi-View Streaming Applications

Quality of Experience (QoE) in multi-view streaming systems is known to be severely affected by the latency associated with view-switching procedures. Anticipating the navigation intentions of the viewer on the multi-view scene could provide the means to greatly reduce such latency. The research work presented in this article builds on this premise by proposing a new predictive view-selection mechanism. A VGG16-inspired Convolutional Neural Network (CNN) is used to identify the viewer’s focus of attention and determine which views would be most suited to be presented in the brief term, i.e., the near-term viewing intentions. This way, those views can be locally buffered before they are actually needed. To this aim, two datasets were used to evaluate the prediction performance and impact on latency, in particular when compared to the solution implemented in the previous version of our multi-view streaming system. Results obtained with this work translate into a generalized improvement in perceived QoE. A significant reduction in latency during view-switching procedures was effectively achieved. Moreover, results also demonstrated that the prediction of the user’s visual interest was achieved with a high level of accuracy. An experimental platform was also established on which future predictive models can be integrated and compared with previously implemented models.info:eu-repo/semantics/publishedVersio