Opt360: QoE Optimization for 360° Video Streaming
Bibliographic record
Abstract
360° video streaming is central to immersive applications such as virtual reality, education, and telepresence, yet delivering stall-free playback with high viewport quality remains difficult under fluctuating bandwidth and inaccurate viewport prediction. Prior solutions either fail to guarantee stall-free playback, introduce prohibitive overhead, or neglect prediction inaccuracies. We propose Opt360, a DASH-compliant optimization framework that generalizes tile assignment into multi-tier priority zones, incorporates prediction accuracy and window length directly into the optimization, and enforces hard constraints on stalls and quality switches. The resulting mixedinteger formulation, coupled with a segment-internal tile scheduler, adapts to diverse viewport models while remaining real-time feasible. Extensive evaluations demonstrate that Opt360 ensures smooth playback, remains resilient to viewport variations, and effectively utilizes bandwidth for improved video quality, even under challenging network conditions.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".