Bibliographic record
Abstract
In conventional HTTP adaptive streaming (HAS), a video content is encoded into multiple representations, and each of these representations is temporally segmented into small pieces. A streaming client makes its segment selections among the available representations mostly according to the measured network bandwidth and buffer fullness. This greatly simplifies the adaptation algorithm design, however, it does not optimize the viewer quality experience. Through experiments, we show that quality fluctuation is a common problem in HAS systems. Frequent and substantial fluctuations in quality are undesired and cause dissatisfaction, leading to revenue loss in both managed and unmanaged video services. In this paper, we argue that the impact of such quality fluctuations can be reduced if the natural variability of video content is taken into consideration. First, we examine the problem in detail and lay out a number of requirements to make such system work in practice. Then, we study the design of a client rate adaptation algorithm that yields consistent video quality even under varying network conditions. We show several results from experiments with a prototype solution. Our approach is an important step towards quality-aware HAS systems. A production-grade solution, however, needs better quality models for adaptive streaming. From this viewpoint, our study also brings up important questions for the community.
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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 | 0.001 | 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 | 0.001 | 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".