A CBR-streaming scheme for VBR-encoded videos
Bibliographic record
Abstract
Media files are required to use variable-bit-rate (VBR) encoding, such as MPEG-2, in order to get constant quality compressed video. However, it produces traffic burst, which makes streaming complicated. Large network latency and jitter cause long start-up delay and frequent unwanted pauses in media playback, respectively. In this paper, we have proposed a proxy based constant-bit-rate (CBR) streaming scheme that allows a server to transmit a VBR-encoded video at a fixed rate, close to its mean encoding bit rate, and deals with the network latency and jitter issues efficiently without caching an entire media file. We have used smoothing buffers at the proxy to eliminate jitter and traffic burst effects. We have used prefix buffers at the proxy to cache the prefixes of popular videos to minimize the start-up delay and to enable near mean bit rate streaming from the server as well as from the proxy. Simulation results are presented to demonstrate the effectiveness of our streaming scheme.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".