Quality of Experience for 3-D Immersive Media Streaming

Abstract

Recent advances in media capture and processing technologies have enabled new forms of true 3-D media content that increase the degree of user immersion. The demand for more engaging forms of entertainment means that content distributors and broadcasters need to fine-tune their delivery mechanisms over the Internet as well as develop new models for quantifying and predicting user experience of these new forms of content. In the work described in this paper, we undertake one of the first studies into the quality of experience (QoE) of real-time 3-D media content streamed to virtual reality (VR) headsets for entertainment purposes, in the context of game spectating. Our focus is on tele-immersive media that embed real users within virtual environments of interactive games. A key feature of engaging and realistic experiences in full 3-D media environments, is allowing users unrestricted viewpoints. However, this comes at the cost of increased network bandwidth and the need of limiting network effects in order to transmit a realistic, realtime representation of the participants. The visual quality of 3-D media is affected by geometry and texture parameters while the temporal aspects of smooth movement and synchronization are affected by lag introduced by network transmission effects. In this paper, we investigate varying network conditions for a set of tele-immersive media sessions produced in a range of visual quality levels. Further, we investigate user navigation issues that inhibit free viewpoint VR spectating of live 3-D media. After reporting on a study with multiple users we analyze the results and assess the overall QoE with respect to a range of visual quality and latency parameters. We propose a neural network QoE prediction model for 3-D media, constructed from a combination of visual and network parameters.

An Innovative Machine-Learning-Based Scheduling Solution for Improving Live UHD Video Streaming Quality in Highly Dynamic Network Environments

“…Consequently, to be able to accommodate all these new immersive live streaming applications, known for being bandwidthhungry and having low-latency and packet loss requirements [14], advanced solutions must be adopted to maintain increased QoE for end-users, since QoE is expected to become the biggest differentiator between network operators [15].…”

Towards Low-Latency Service Delivery in a Continuum of Virtual Resources: State-of-the-Art and Research Directions

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“…Simulations have shown that their PVRV system improves energy efficiency and the quality of received viewport over conventional methods. QoE of VR streaming services has also been addressed in [176]. Doumanoglou, A., et al have studied the QoE of real-time 3D media content streamed to VR headsets for entertainment purposes.…”

Why is Multimedia Quality of Experience Assessment a Challenging Problem?

“…The proposed model can be employed by operators to evaluate the performance of networks supplying YouTube videos. Also, works have focused on immersive QoE, e.g., Doumanoglou et al [68] studied neural networks based immersive QoE prediction of real-time 3D media content streamed to VR headsets for entertainment purposes. Other studies tried to recognize the interrelation between QoE and QoS.…”