Tile-Weighted Rate-Distortion Optimized Packet Scheduling for 360° Virtual-Reality Video Streaming
Why this work is in the frame
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Bibliographic record
Abstract
A key challenge of 360° virtual-reality (VR) video streaming is ensuring high quality with limited network bandwidth. Currently, most of the studies focus on tile-based adaptive bit-rate streaming to reduce bandwidth consumption, where resources in network nodes are not fully utilized. This article proposes a tile-weighted rate-distortion (TWRD) packet scheduling optimization system to reduce data volume and improve video quality. A multimodal spatial–temporal attention transformer is proposed to predict viewpoint with probability that is used to dynamically weight tiles and their corresponding packets. The packet scheduling problem of determining which packets should be dropped is formulated as an optimization problem solved by a dynamic programming solution. Experiment results demonstrate that the proposed method outperforms the existing methods under various conditions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 it