Enhancing Low Latency Adaptive Live Streaming Through Precise Bandwidth Prediction
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
To ensure high performance for HTTP adaptive streaming (HAS), it is critical to provide accurate prediction of end-to-end network bandwidth. Low Latency Live Streaming (LLLS), which has been gaining popularity, faces even greater challenges in this regard. Unlike Video-on-Demand (VOD) streaming, which only needs long-term bandwidth prediction and can tolerate some prediction errors, LLLS demands precise short-term bandwidth predictions. These challenges are amplified by the fact that short-term bandwidth experiences both large abrupt changes and uncertain fluctuations. Furthermore, obtaining valid bandwidth measurement samples in LLLS poses difficulties due to the on-off traffic pattern. In this work, we present DeeProphet, a system designed to enhance the performance of LLLS by achieving accurate bandwidth prediction. DeeProphet collects valid bandwidth samples by identifying intervals of packet continuous sending leveraging TCP state information, estimates the segment-level bandwidth robustly by filtering out noisy samples, and predicts both significant changes and uncertain fluctuations in future bandwidth by combining both time series and learning-based models. Experimental results demonstrate that DeeProphet effectively enhances the overall Quality of Experience (QoE) by 39.5% to 464.6% compared to state-of-the-art LLLS Adaptive Bitrate (ABR) algorithms.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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