A performance evaluation of mobility management and multihop supplying partner strategies for 3D streaming systems over thin mobile devices
Bibliographic record
Abstract
Summary The recent advances in technology and mobile computing led to the rapid growth of networked 3D streaming applications. The emerging services can involve augmented reality, virtual environment walkthrough, multiplayer gaming just to mention a few. Because of the limited network bandwidth of the client‐server approach, research works are now turning toward mobile ad hoc networks‐based streaming, where the resources of each peer are used during the streaming service. Peer‐to‐peer technologies are considering the solution to adapt for scalable applications. Yet, supplying partner selection and 3D data delivery are still significant challenges to face because of the dynamic wireless environment that causes link breakages, high packet loss, an adverse impact on the quality of the 3D media, and a low user satisfaction. In this paper, we propose a supplying partner selection technique coupled with a content delivery technique for peer‐to‐peer 3D streaming over thin mobile devices. Our proposed protocol, which we refer to as MULTIPLY, considers multihop suppliers in order to alleviate the load on the server and uses the signal strength measurement to analyze the wireless link when sending back the 3D data. Given the high dynamicity of the network due to the mobility of the users, the streaming can be greatly affected. We therefore study the impact of the mobility on MULTIPLY. The performance evaluation of our protocol obtained using an extensive set of simulation experiments is then reported. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.003 |
| Open science | 0.000 | 0.000 |
| 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".