Towards QoE-aware HAS video streaming over LTE
Bibliographic record
Abstract
Lately, HTTP Adaptive Streaming (HAS) has become dominant among different video streaming techniques. However, owing to the unpredictable nature of the wireless radio channel and device mobility, using HAS over mobile wireless networks is still very challenging. In this paper, we propose a novel Quality of Experience (QoE) optimization mechanism for HAS in the context of mobile wireless networks. The proposed mechanism leverages recent advances in HAS specification, which includes new features for QoE measurements and reporting. First, we formulate a discrete optimization problem aiming at maximizing the overall average quality, and minimizing the negative impact of temporal video quality changes for all HAS users simultaneously. Second, in order to take advantage of well-known continuous optimization techniques and to decrease the computational complexity, we convert the formulated problem into a continuous form, and propose a gradient based algorithm to solve the continuous optimization problem. The results of our simulations demonstrate that our system attained better perceived video quality by almost 8% on average, while lowering the freezing period by 20% on average across HAS users when compared to other approaches where HAS users only rely on local adaptation logics.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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 teacher head, 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".