MétaCan
Menu
Back to cohort
Record W2126624451 · doi:10.1109/icassp.2008.4517813

Joint media-channel aware unequal error protection for wireless scalable video streaming

2008· article· en· W2126624451 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceComputer networkChannel (broadcasting)Coding (social sciences)Network packetLink adaptationScalabilityJoint (building)Decoding methodsVideo qualityWirelessReal-time computingScalable Video CodingCoding tree unitChannel codeAlgorithmTelecommunicationsEngineeringFading

Abstract

fetched live from OpenAlex

In this paper, we propose a joint source-channel unequal error protection scheme for scalable video streaming over capacity constrained high speed packet access (HSPA) networks. Conventional link adaptation schemes in HSPA networks use the modulation and coding scheme (MCS) that achieves a preset channel frame error rate. Our scheme utilizes video priority information along with channel quality information to set the channel coding rate that maximizes the cumulative coding rate of channel coding and application layer unequal error protection. Performance evaluations show that that under the same constraints our scheme results in an average performance improvement of 0.5dB in video PSNR for different channel conditions and different video sequences.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.634
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.256
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it