A Classification-Based Path Selection Scheme for Video Streaming over Multi-Hop Networks
Bibliographic record
Abstract
A classification-based method is proposed to select an optimal path for video streaming over multi-hop mesh networks. Our main contribution is translating of path selection over multi-hop networks to a standard classification problem. The classification is based on minimizing average video packet distortion at the receiving nodes. We also consider long range dependence (LRD) characteristics of variable bit rate (VBR) video encoders by modeling the link as a fractional Brownian motion queue. In our method, a support vector machine is applied at each hop to optimally assign the next hop (partial path) for each video packet received at that hop. Wireless channel conditions (WCC) including packet loss probability of the channel and maximum achievable rate are used as class prototypes. Sample feature vectors include video content features (VCF) and video encoding parameters (VEP) extracted from video sequences. The classifiers are trained offline using a vast collection of video sequences and wireless channel conditions in order to yield optimal performance during real time path selection. Our method substantially reduces the complexity of conventional exhaustive optimization methods and results in high quality (low distortion). Simulations are conducted over an elementary multi-hop structure and cascades of such structure which not only proves the superiority of our method in terms of low complexity and high peak signal to noise ratio (PSNR) performance, but also provides important insights that can guide the design of network infrastructures and streaming protocols for video streaming.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".