Delay-Optimized Multi-User VR Streaming via End-Edge Collaborative Neural Frame Interpolation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this article, with the objective of significantly increasing the frame rate of virtual reality (VR) videos, we design an efficient end-edge collaborative VR streaming system which consists of three modules: frame similarity analysis, offloading decision making, and collaborative frame interpolation. In specific, frame similarity analysis tries to eliminate redundant frames based on perceived quality assessment, so that the required number of interpolated frames can be reduced without deteriorating visual quality. Then, an end-to-end (E2E) delay optimization problem is formulated to obtain the optimal offloading strategy, by balancing the transmission and computing burden of neural frame interpolation via end-edge collaboration. Furthermore, the E2E delay of the proposed system is theoretically analyzed based on queuing theory. Our analysis reveals that, the proposed collaborative distribution of interpolation tasks between edge and end devices are effective to achieve the minimal E2E delay of streaming VR videos. Extensive experimental results demonstrate that the proposed system can significantly improve the frame rate of VR videos, while maintaining timely VR content delivery in various networking 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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