Efficient stereo segment scheduling in peer-to-peer 3D/multi-view video streaming
Bibliographic record
Abstract
3D (or stereo) video has been a visually appealing and costly affordable technology. More sophisticated multi-view videos have also been demonstrated. Yet their remarkably increased data volume poses greater challenges to the conventional client/server streaming systems, which has already suffered from supporting 2D videos. The stringent multi-stream synchronization further complicate the system design. In this paper, we present an initial attempt toward efficient streaming of stereo/multi-view videos over a peer-to-peer network. We show that the inherent multi-stream nature of stereo video makes segment scheduling more difficult, which is particularly acute with the existence of multiple senders in a peer-to-peer overlay. We formulate the stereo segment scheduling problem as a Binary Quadratic Programming problem and optimally solve it using an MIQP solver. However, given the high peer dynamics and the stringent playback deadline in real-time streaming, the optimal solution is too costly to be obtained. Thus, we develop two efficient algorithms to allow peers frequently compute the scheduling. We show that one of the proposed algorithms can achieve an analytical guarantee in the worst case performance, in particular, the approximation factor is at most 3 comparing with the optimal solution. We implement the proposed algorithms and the optimal in a peer-to-peer simulating system, and show that the proposed algorithms can achieve near-optimal performance efficiently. We further implement two other scheduling algorithms that are used in popular peer-to-peer streaming systems for comparison, and extend our design to support multi-view video with view diversity and dynamics. Under different end-system and network configurations with both stereo and multi-view streaming, the simulation results demonstrate that our algorithms outperform others in terms of streaming quality, stream synchronization/smoothness and scalability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".