Balancing Interruption Frequency and Buffering Penalties in VBR Video Streaming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main goal of a streaming application is to enable the successful decoding of each video object before its displaying deadline is violated, and to recover from a deadline violation properly. Hence, we define the main performance metric of a streaming system as the number of interruptions during a video presentation, or the number of jitters. Previous literature has described solutions to estimate the jitter-free probability for an entire video segment. In this work, we present a novel analytical framework, which requires only a Markov Variable Bit Rate (VBR) channel model, to study the frequency of jitters under the constraint of initial playback delay, receiver buffer size, and different jitter recovering schemes. Both the infinite and finite buffer cases are considered. This technique is then applied to investigate streaming over a wireless system modeled by an extended Gilbert channel with ARQ transmission control. Experimental results with MPEG-4 VBR encoded video validate our analysis. Finally, we show that the proposed analysis provides a theoretical foundation to quantify the tradeoffs between the jitter frequency, jitter recovering delay, initial delay, and the receiver buffer size for a general class of VBR streaming over random VBR channels with different jitter recovering schemes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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