Content Adaptive Encoding For Interactive Game Streaming
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911291616002048 |
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| author | Soltanayev, Shakarim Zisimopoulos, Odysseas Anam, Mohammad Ashraful Kung, Man Cheung Katsenou, Angeliki Andreopoulos, Yiannis |
| author_facet | Soltanayev, Shakarim Zisimopoulos, Odysseas Anam, Mohammad Ashraful Kung, Man Cheung Katsenou, Angeliki Andreopoulos, Yiannis |
| contents | Video-on-demand streaming has benefitted from \textit{content-adaptive encoding} (CAE), i.e., adaptation of resolution and/or quantization parameters for each scene based on convex hull optimization. However, CAE is very challenging to develop and deploy for interactive game streaming (IGS). Commercial IGS services impose ultra-low latency encoding with no lookahead or buffering, and have extremely tight compute constraints for any CAE algorithm execution. We propose the first CAE approach for resolution adaptation in IGS based on compact encoding metadata from past frames. Specifically, we train a convolutional neural network (CNN) to infer the best resolution from the options available for the upcoming scene based on a running window of aggregated coding block statistics from the current scene. By deploying the trained CNN within a practical IGS setup based on HEVC encoding, our proposal: (i) improves over the default fixed-resolution ladder of HEVC by 2.3 Bjøntegaard Delta-VMAF points; (ii) infers using 1ms of a single CPU core per scene, thereby having no latency overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22327 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Content Adaptive Encoding For Interactive Game Streaming Soltanayev, Shakarim Zisimopoulos, Odysseas Anam, Mohammad Ashraful Kung, Man Cheung Katsenou, Angeliki Andreopoulos, Yiannis Image and Video Processing Computer Vision and Pattern Recognition Video-on-demand streaming has benefitted from \textit{content-adaptive encoding} (CAE), i.e., adaptation of resolution and/or quantization parameters for each scene based on convex hull optimization. However, CAE is very challenging to develop and deploy for interactive game streaming (IGS). Commercial IGS services impose ultra-low latency encoding with no lookahead or buffering, and have extremely tight compute constraints for any CAE algorithm execution. We propose the first CAE approach for resolution adaptation in IGS based on compact encoding metadata from past frames. Specifically, we train a convolutional neural network (CNN) to infer the best resolution from the options available for the upcoming scene based on a running window of aggregated coding block statistics from the current scene. By deploying the trained CNN within a practical IGS setup based on HEVC encoding, our proposal: (i) improves over the default fixed-resolution ladder of HEVC by 2.3 Bjøntegaard Delta-VMAF points; (ii) infers using 1ms of a single CPU core per scene, thereby having no latency overhead. |
| title | Content Adaptive Encoding For Interactive Game Streaming |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.22327 |