The impact of video-quality-level switching on user quality of experience in dynamic adaptive streaming over HTTP
Bibliographic record
Abstract
Dynamic adaptive streaming over HTTP (DASH) has become a promising solution for video delivery services over the Internet in the last few years. Currently, several video content providers use the DASH solution to improve the users’ quality of experience (QoE) by automatically switching video quality levels (VQLs) according to the network status. However, the frequency of switching events between different VQLs during a video streaming session may disturb the user’s visual attention and therefore affect the user’s QoE. As one of the first attempts to characterize the impact of VQL switching on the user’s QoE, we carried out a series of subjective tests, which show that there is a correlation between the user QoE and the frequency, type, and temporal location of the switching events. We propose a novel parameter named switching degradation factor (SDF) to capture such correlation. A DASH algorithm with SDF parameter is compared with the same algorithm without SDF. The results demonstrate that the SDF parameter significantly improves the user’s QoE, especially when network conditions vary frequently.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".