QoS Improvement for Video Streaming over MANET Using Network-Coding
Bibliographic record
Abstract
Video streaming (like YouTube) services and related applications become more and more widespread. Therefore, video streaming delivery over a mobile ad-hoc network (MANET) becomes a necessity as an important content delivery infrastructure between the user (content consumer) and the content storage node. Furthermore, user mobility impacts the quality of the delivered video and hence new concepts should be considered. Accordingly, the innovative concept on network coding (NC) emerges as a promising approach for improving the video transmission quality mainly in multicast environment. In this paper, we focus on Quality of Service (QoS) improvement for video streaming over MANET using random network coding. Basically, we consider video coded by H264/SVC codec that generates packets with different priorities and uses the IEEE 802.11e MAC for traffic differentiation. A successful transmission of high priority packets leads to enhance the video transmission quality. Accordingly, we propose a transmission scheme to protect high priority packets from being lost. Our approach, named Multicast Scalable Video Transmission using Classification-Scheduling Algorithms and Network Coding over MANET (and denoted MSVT_CSA_NC), adopts a cross layer solution between the H.264/SVC codec, the network and MAC layers. Moreover, our delivery mechanisms based on random network coding ensure high throughput and low network load over MANET. Simulation results confirm the substantial performance improvement brought by our approach.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".