Packet Loss Recovery in Broadcast for Real-Time Applications in Dense Wireless Networks
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
Packet loss recovery in wireless broadcast is challenging, particularly for real-time applications which have strict and short delivery deadline. To recover the maximum number of lost packets within a short time, existing packet recovery solutions often rely on instantly decodable network coding (IDNC). Some of these solutions can recover nearly the maximum number of lost packets possible at the cost of collecting feedback from all (or a large percentage of) users. This is impractical in dense networks. In addition, their runtime grows with the number of users, which is not desirable due to the urgent delivery deadline of real-time applications. In this work, we introduce random instantly decodable network coding (RIDNC), a random encoding approach to IDNC. We propose RAndom IDNCEncoder (RACE), a fast RIDNC encoder that can recover nearly as many lost packets as the optimal RIDNC encoder. We compare RACE with the CrowdWiFi encoder, a high performing packet loss recovery solution used in CrowdWiFi, a commercial system for broadcasting live video in dense networks. We show that RACE is up to two orders of magnitude faster than the CrowdWiFi encoder, and recovers more lost packets in practice, where there is not enough time to collect feedback from many users.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.003 | 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 it