A First Look at Open-GoP Streaming with Av1 S-Frames
Bibliographic record
Abstract
Recent improvements in adaptive video streaming have been significant in the areas of adaptation algorithms and encoding (e.g., emergence of AV1 codec). A promising but yet unexplored feature of AV1 is the switching frames (S-frames). In this paper, we explore the pros and cons of S-frames in DASH streaming and demonstrate that S-frames can improve the compression and quality switching capabilities of AV1. We conduct a measurement study to understand the extent of benefits of S-frames and find that 10% – 50% data savings are achievable for many encoding configurations where S-frame use is maximized. We further propose a stream structure where the number of S-frames used balances QoE improvement with the ability to play or seek randomly within the video in challenging network conditions. We evaluate the streams using S-frames in a DASH player under a variety of conditions. We find that when S-frame use and switching opportunities are both maximized, they significantly improve stall performance, the critical aspect of the Quality of Experience (QoE), by reducing stall time by at least 88 % in realistic bandwidth conditions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".