Bibliographic record
Abstract
Today, live streaming is gaining a rapid growth in use, which refers to streaming the media content recorded and broadcast in real time. In live streaming, latency is of utmost importance since smaller latency means higher user engagement. HTTP adaptive streaming (HAS) is now the most popular live streaming technology, where the video client sends HTTP requests to server to download video segments. The bitrate adaptation (ABR) algorithm inside the client determines bitrate level for every segment. It is of great help for ABR algorithm to quantify the influence of different HAS factors on streaming performance. However, existing work mainly focuses on video on demand (VoD) streaming rather than live streaming. In this paper, we theoretically analyze live streaming performance. We first establish a queuing model to describe playout buffer evolution. Based on the model, we respectively characterize rebuffering probability, rebuffering count and streaming latency, and analyze the effects of chunk arrival rate, arrival interval fluctuation, startup threshold and video skipping on them. From analysis results, we propose insights and recommendations for bitrate adaptation in live streaming and design a simple heuristic ABR algorithm leveraging them. Extensive simulations verify the insights as well as effectiveness of the designed algorithm.
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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 | 0.001 |
| 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 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".