EyeNexus: Adaptive Gaze-Driven Quality and Bitrate Streaming for Seamless VR Cloud Gaming Experiences
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911155333627904 |
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| author | Wu, Ze Alhilal, Ahmad Tsui, Yuk Hang Siekkinen, Matti Hui, Pan |
| author_facet | Wu, Ze Alhilal, Ahmad Tsui, Yuk Hang Siekkinen, Matti Hui, Pan |
| contents | Virtual Reality (VR) cloud gaming systems render the 3D graphics on cloud servers for playing graphically demanding games on VR headsets. Delivering high-resolution game scenes is challenging due to variation in network performance. By leveraging the non-uniform human vision perception, foveated rendering and encoding have proven effective for optimized streaming in constrained networks. SoTA foveation methods either do not incorporate real-time gaze data or are unable to handle variations in network conditions, resulting in a suboptimal user experience. We introduce EyeNexus, a pioneering system that combines real-time gaze-driven spatial compression (FSC) with gaze-driven video encoding (FVE), transforming the gaze point for precise alignment and foveation. We propose a novel foveation model that dynamically adjusts the foveation region based on real-time bandwidth and gaze data. The model simplifies network-aware quality assignment in FVE, ensuring smooth and imperceptible quality gradients. We evaluate EyeNexus using objective and subjective measures with different network conditions and games. EyeNexus reduces latency by up to 70.9% and improves perceptual visual quality by up to 24.6%. Our IRB-approved user study shows that EyeNexus achieves the highest playability and visual quality, with improvements of up to 48%, while eliminating motion sickness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11807 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EyeNexus: Adaptive Gaze-Driven Quality and Bitrate Streaming for Seamless VR Cloud Gaming Experiences Wu, Ze Alhilal, Ahmad Tsui, Yuk Hang Siekkinen, Matti Hui, Pan Image and Video Processing Multimedia Virtual Reality (VR) cloud gaming systems render the 3D graphics on cloud servers for playing graphically demanding games on VR headsets. Delivering high-resolution game scenes is challenging due to variation in network performance. By leveraging the non-uniform human vision perception, foveated rendering and encoding have proven effective for optimized streaming in constrained networks. SoTA foveation methods either do not incorporate real-time gaze data or are unable to handle variations in network conditions, resulting in a suboptimal user experience. We introduce EyeNexus, a pioneering system that combines real-time gaze-driven spatial compression (FSC) with gaze-driven video encoding (FVE), transforming the gaze point for precise alignment and foveation. We propose a novel foveation model that dynamically adjusts the foveation region based on real-time bandwidth and gaze data. The model simplifies network-aware quality assignment in FVE, ensuring smooth and imperceptible quality gradients. We evaluate EyeNexus using objective and subjective measures with different network conditions and games. EyeNexus reduces latency by up to 70.9% and improves perceptual visual quality by up to 24.6%. Our IRB-approved user study shows that EyeNexus achieves the highest playability and visual quality, with improvements of up to 48%, while eliminating motion sickness. |
| title | EyeNexus: Adaptive Gaze-Driven Quality and Bitrate Streaming for Seamless VR Cloud Gaming Experiences |
| topic | Image and Video Processing Multimedia |
| url | https://arxiv.org/abs/2509.11807 |