On the design of algorithms for mobile multimedia systems: A survey
Bibliographic record
Abstract
SUMMARY In the past few years, we have seen a global flurry of the Internet in the rapid roll‐out of multimedia—the commercial products such as PPlive, YouTube, and Skype have occupied a large portion of Internet bandwidth. One reason behind the continual growth of multimedia services is due to the increasingly deployed and offered broadband networks. Besides the traditional wired end users, Internet service providers (ISPs) are expected to provide multimedia services, especially video streaming, to wireless end users as well in that it allows ISPs to strengthen their competitiveness by offering these new services. However, the existing solutions to multimedia in wired networks cannot directly apply to wireless networks with lower bandwidth, higher latency, and higher burst error rate. Furthermore, these services could suffer from user's mobility and the heterogeneity caused by different wireless technologies (e.g. CDMA2000, WCDMA, TD‐SCDMA, Wi‐Fi, Long‐Term Evolution (LTE), and WiMAX). This paper surveys several key issues of mobile multimedia, focusing on multirate multicast, scalable video coding (SVC), and QoS management. Besides, the opportunities as well as the challenges of providing multimedia services in the next generation wireless mobile systems—3GPP LTE—are investigated. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".