Meaning over Motion: A Semantic-First Approach to 360° Viewport Prediction
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912810803396608 |
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| author | Khah, Arman Nik Bahreini, Arvin Prakash, Ravi |
| author_facet | Khah, Arman Nik Bahreini, Arvin Prakash, Ravi |
| contents | Ultra-high-resolution 360-degree video streaming is severely constrained by the massive bandwidth required to deliver immersive experiences. Current viewport prediction techniques predominately rely on kinematics or low-level visual saliency, treating users as passive physical objects governed by inertia. This theoretical limitation leads to the "Saccade Trap" -- a critical failure mode where predictors fail to anticipate rapid, meaning-driven shifts in attention, causing rebuffering stalls exactly when user engagement is highest. To resolve this, we propose Semantically-Adaptive Conformal Tiling with Associative Lookahead, a novel framework that integrates cognitive intent into network control. Unlike "one-size-fits-all" approaches, our method utilizes an architectural inversion strategy: heavy semantic reasoning is offloaded to the server to generate lightweight association graphs, which guide a low-latency client-side controller. We construct a personalized Multi-Modal Prediction Set that dynamically tightens safety margins during stable fixation to maximize efficiency, while simultaneously pre-fetching non-adjacent tiles containing semantically linked objects (Associative Lookahead). This mechanism effectively converts the "calm" of fixation into a preparation phase for the next interaction. Trace-driven evaluation on the 360-AV-HM dataset demonstrates that this approach successfully mitigates the Saccade Trap, reducing stall duration by $\ge$ 20% and lowering effective bandwidth consumption by $\ge$ 18% compared to state-of-the-art trajectory-based baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05416 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Meaning over Motion: A Semantic-First Approach to 360° Viewport Prediction Khah, Arman Nik Bahreini, Arvin Prakash, Ravi Multimedia 68T45, 68M10 H.5.1; I.2.10 Ultra-high-resolution 360-degree video streaming is severely constrained by the massive bandwidth required to deliver immersive experiences. Current viewport prediction techniques predominately rely on kinematics or low-level visual saliency, treating users as passive physical objects governed by inertia. This theoretical limitation leads to the "Saccade Trap" -- a critical failure mode where predictors fail to anticipate rapid, meaning-driven shifts in attention, causing rebuffering stalls exactly when user engagement is highest. To resolve this, we propose Semantically-Adaptive Conformal Tiling with Associative Lookahead, a novel framework that integrates cognitive intent into network control. Unlike "one-size-fits-all" approaches, our method utilizes an architectural inversion strategy: heavy semantic reasoning is offloaded to the server to generate lightweight association graphs, which guide a low-latency client-side controller. We construct a personalized Multi-Modal Prediction Set that dynamically tightens safety margins during stable fixation to maximize efficiency, while simultaneously pre-fetching non-adjacent tiles containing semantically linked objects (Associative Lookahead). This mechanism effectively converts the "calm" of fixation into a preparation phase for the next interaction. Trace-driven evaluation on the 360-AV-HM dataset demonstrates that this approach successfully mitigates the Saccade Trap, reducing stall duration by $\ge$ 20% and lowering effective bandwidth consumption by $\ge$ 18% compared to state-of-the-art trajectory-based baselines. |
| title | Meaning over Motion: A Semantic-First Approach to 360° Viewport Prediction |
| topic | Multimedia 68T45, 68M10 H.5.1; I.2.10 |
| url | https://arxiv.org/abs/2601.05416 |