A scalable multimedia streaming scheme with CBR‐transmission of VBR‐encoded videos over the internet
Bibliographic record
Abstract
Streaming audio/video contents over the Internet requires large network bandwidth and timely delivery of media data. A streaming session is generally long and also needs a large I/O bandwidth at the streaming server. A streaming server, however, has limited network and I/O bandwidth. For this reason, a streaming server alone cannot scale a streaming service well. An entire audio/video media file often cannot be cached due to intellectual property right concerns of the content owners, security reasons, and also due to its large size. This makes a streaming service hard to scale using conventional proxy servers. Media file compression using variable‐bit‐rate (VBR) encoding is necessary to get constant quality video playback although it produces traffic bursts. Traffic bursts either waste network bandwidth or cause hiccups in the playback. Large network latency and jitter also cause long start‐up delay and unwanted pauses in the playback, respectively. In this paper, we propose a proxy based constant‐bit‐rate (CBR)‐transmission scheme for VBR‐encoded videos and a scalable streaming scheme that uses a CBRtransmission scheme to stream stored videos over the Internet. Our CBR‐streaming scheme allows a server to transmit a VBRencoded video at a constant bit rate, close to its mean encoding bit rate, and deals with the network latency and jitter issues efficiently in order to provide quick and hiccup free playback without caching an entire media file. Our scalable streaming scheme also allows many clients to share a server stream. We use 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. We use smoothing buffers at the proxy not only to eliminate jitter and traffic burst effects but also to enable many clients to share the same server stream. We present simulation results 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".