Interacting With New York City Data by HoloLens Through Remote Rendering

Long, Zijian; Dong, Haiwei; Saddik, Abdulmotaleb El

doi:10.1109/mce.2022.3165961

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Metaverse-ce Taxonomy1

Xr -Network Integration a Network-aware Xr System1

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2022

2023

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Cited by 7 publications

(2 citation statements)

References 13 publications

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“…• Vision, Sound, Haptics, Smell, Taste: The users look at a binocular 3D high-resolution displays by an eXtended reality headset or a smartphone [3]. Broader field of view (130 • horizontally by 100 • vertically or even 200 • by 130 • ), higher resolution (4K or even 8K), and higher refresh rate (90 fps or even 120 fps) provide a natural view to the users.…”

Section: Metaverse-ce Taxonomymentioning

confidence: 99%

Metaverse Meets Consumer Electronics

Dong

Liu²

2023

IEEE Consumer Electron. Mag.

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Section: Metaverse-ce Taxonomymentioning

confidence: 99%

Metaverse Meets Consumer Electronics

Dong

Liu²

2023

IEEE Consumer Electron. Mag.

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“…• Action: Bitrate of the XR packets controlled by the XR headset end-host. • Reward: A QoE metric composed of the factors discussed in Section II-D. An example of the reward function [7] could be the combination of reward from the smoothness (i.e., the change of framerate), reward from the quality of the XR display (i.e., the change of resolution), and penalty from the latency. • RL algorithm: If each RL agent is assumed responsible for one XR end-host device, multiple XR devices would require multiple RL agents working together.…”

Section: Xr -Network Integration a Network-aware Xr Systemmentioning

confidence: 99%

The Metaverse from a Multimedia Communications Perspective

Dong,

Lee

2023

Preprint

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eXtended reality (XR) technologies such as virtual reality and 360°stereoscopic streaming enable the concept of the Metaverse, an immersive virtual space for collaboration and interaction. To ensure high fidelity display of immersive media, the bandwidth, latency and network traffic patterns will need to be considered to ensure a user's Quality of Experience (QoE). In this article, examples and calculations are explored to demonstrate the requirements of the abovementioned parameters. Additionally, future methods such as network-awareness using reinforcement learning (RL) and XR content awareness using spatial or temporal difference in the frames could be explored from a multimedia communications perspective.

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