Optimal Packet Scheduling for Multi-Description Multi-Path Video Streaming Over Wireless Networks
Bibliographic record
Abstract
As developments in wireless networks continue, there is an increasing expectation with regard to supporting high- quality real-time video streaming service in such networks. The recent advances in multi-description (MD) multi-path transport has made it a promising technology for content-rich wireless multimedia communications. This paper presents a rate-distortion (R-D) optimized packet scheduling algorithm (OPT- MD) for streaming MD-coded video along multiple wireless paths. Our algorithm relies on R-D hint information that is used to characterize a packet in a R-D sense. The information consists of the size of the packet in bits and the importance of the packet for reconstructing the video. Each of the video description adaptively selects certain important packets for transmission according to the quality of the transmission path by simultaneously considering bandwidth, bit error rate, and delay so that the overall end-to-end video distortion in terms of the mean square error (MSE) is minimized. Extensive simulation results demonstrate that OPT-MD can improve the quality of video streaming significantly as compared to a conventional scheduling approach that does not consider the relative importance of the video packets and the channel conditions (RANDOM-MD). The gains in performance reach up to 5 dB and 4 dB for streaming MD-coded format QCIFFORMANandTABLEvideo sequences, respectively, in the scenario of adaptation to a simulated time- varying network channel. Our efforts in this work provides an important methodology for high-quality real-time video streaming applications over wireless networks.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".