Transmission Protocol Customization for On-demand Tile-based 360° VR Video Streaming
Bibliographic record
Abstract
Tile-based streaming has been proposed to address the challenge of high transmission rate demand in 360° virtual reality (VR) video streaming. However, it suffers from network condition dynamics and viewer field-of-view (FoV) prediction errors. In this paper, we propose a customized transmission protocol based on Quick UDP Internet Connections (QUIC) which operates over a VR video core network slice to support enhanced slice-level VR video data transmission. The QUIC protocol is tailored to accommodate the characteristics of tile-based VR video streaming where explicit mapping relations between requested video tiles and QUIC streams are established. Two customized protocol functionalities including packet filtering and caching-based packet retransmission are proposed, which filter out outdated video data due to FoV prediction errors and achieve efficient packet loss recovery, respectively. A slice-level packet header is designed to support enhanced slice-based VR video packet transmissions with the proposed protocol functionalities. Simulation results are presented to demonstrate the effectiveness of our proposed protocol.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".