Impact of Network Dynamics on User's Video Quality: Analytical Framework and QoS Provision
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Bibliographic record
Abstract
<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We develop an analytical framework to investigate the impacts of network dynamics on the user perceived video quality. Our investigation stands from the end user's perspective by analyzing the receiver playout buffer. In specific, we model the playback buffer at the receiver by a <formula formulatype="inline"> <tex Notation="TeX">$G/G/1/\infty$</tex></formula> and <formula formulatype="inline"> <tex Notation="TeX">$G/G/1/N$</tex></formula> queue, respectively, with arbitrary patterns of packet arrival and playback. We then examine the transient queue length of the buffer using the diffusion approximation. We obtain the closed-form expressions of the video quality in terms of the start-up delay, fluency of video playback and packet loss, and represent them by the network statistics, i.e., the average network throughput and delay jitter. Based on the analytical framework, we propose adaptive playout buffer management schemes to optimally manage the threshold of video playback towards the maximal user utility, according to different quality-of-service requirements of end users. The proposed framework is validated by extensive simulations. </para>
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it