OptVV: An Adaptive Optimization Framework for Volumetric Video Streaming
Bibliographic record
Abstract
The growing demand for interactive and personalized visual experiences, particularly in the metaverse era, highlights the importance of three-dimensional volumetric video. However, its large size and complex processing requirements pose significant challenges for practical streaming, often leading to resource waste and quality issues. To address this, we propose OptVV, an optimization framework for high-quality and bandwidth-efficient volumetric video streaming. The framework comprises three key components: (1) Adaptive QoE optimization, dynamically adjusting video quality based on network conditions, playback requirements, and volumetric video characteristics to ensure smooth playback; (2) an optimized DASH scheme for enhanced quality adaptation and bandwidth efficiency; and (3) optimal resource scheduling for real-time decoding, efficiently managing computational resources to maintain playback smoothness. Evaluation results show that OptVV significantly improves streaming performance, achieving up to 80% bandwidth savings, 177% higher viewport quality, 83% fewer playback stalls, and 72% faster decoding time compared to existing methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".