A performance study of proxy-based TCP rate control design for mobile video streaming services
Bibliographic record
Abstract
Reducing startup delay of video streaming is important for attracting more users. In LTE networks, even though available bandwidth has increased, behavior of TCP, which has a slow start phase and estimates available capacity based on packet loss event, increases the startup delay of video streaming. To solve this issue, we design a proxy-based TCP rate control (PTRC) scheme for achieving low startup delay of mobile video streaming by using the explicit radio related information from a base station (BS). We introduce a target queue length (Qtarget) as feedback information to a proxy, which represents a desired value at the BS. Here, the Qtarget is dynamically calculated by referring to average data rate of a radio link and backhaul delay. Then the proxy is informed of this Qtarget by an in-band signaling message from the BS, and controls its sending rate accordingly. Hence, the proposed PTRC scheme can boost up its transmission rates in the initial phase, and keep instant queue length at the BS close to the Qtarget. We verify that the proposed PTRC scheme achieves about 71.2% reduction of startup delay for mobile video streaming in LTE environment, compared to conventional schemes.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".