Optimal pricing for mobile video streaming
Bibliographic record
Abstract
Mobile phones are among the most popular consumer devices; the recent developments of 3G networks and smart phones enable users to watch video programs by subscribing to data plans from service providers. Because of the ubiquity of mobile phones and phone-to-phone communication technologies, subscribers can redistribute the video content to nonsubscribers. Such a redistribution mechanism is a potential competitor for the service provider and is very difficult to trace, given users' high mobility. The service provider must set a reasonable price for the data plan to prevent such unauthorized redistribution behavior and to protect the provider's own profit. In this chapter, we analyze the optimal price setting for the service provider by investigating the equilibrium between the subscribers and the secondary buyers in the content redistribution network. We model the behavior between the subscribers and the secondary buyers as a noncooperative game and find the optimal price and quantity for both groups of users. Such an analysis can help the service provider preserve the profit under the threat of the redistribution networks and can improve the quality of service for end users. Introduction The explosive advance of multimedia processing technologies is creating dramatic shifts in the ways that video content is delivered to and consumed by end users. Also, the increased popularity of wireless networks and mobile devices has drawn a great deal of attention in the past decade about ubiquitous multimedia access in the multimedia community.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".