Real-time multiple description and layered encoded video streaming with optimal diverse routing
Bibliographic record
Abstract
Multiple description (MD) and layered coding (LC) are two source-coding approaches proposed for adaptive and robust video streaming over lossy networks. Both streaming methods aim to improve the streaming quality by transmitting the generated multiple sub-bitstreams over partially link-disjoint paths. However, the achieved qualities heavily depend on the media characteristics, path conditions and application requirements. In order to attain the highest quality, we study optimal multi-path selection methods for both MD and LC streaming. The simulations run over an overlay infrastructure show 9.0 - 12.5 dB peak signal-to-noise ratio (PSNR) improvement when the video is streamed over intelligently selected multiple paths instead of the shortest path or maximally link-disjoint paths. By the help of the proposed path selection methods, the end users estimate the expected qualities of MD and LC streaming for the given network conditions and application requirements prior to the streaming, which allows the users to make a choice accordingly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".