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Record W2000165099 · doi:10.1109/mass.2011.132

Power Efficient High Quality Multimedia Multicast in LTE Wireless Networks

2011· article· en· W2000165099 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMultimedia Broadcast Multicast ServiceComputer scienceEnodeBComputer networkQuality of experienceVideo qualityMulticastScalabilityQuality of serviceBase stationNetwork packetUser equipment

Abstract

fetched live from OpenAlex

We examine power-efficient high-quality scalable video streaming in LTE networks through its eMBMS service. We consider scalable video streaming and download services offered by eMBMS service over LTE networks. We propose an effective and practical solution to jointly optimize user experience and power consumption in both UE and eNodeB. To perform power efficient multimedia transmission in LTE networks, we face three key trade-offs: (1) maximizing energy saving vs. minimizing delay, (2) maximizing sleep time vs. minimizing lost packets, (3) maximizing quality of video vs. minimizing unnecessary video transmissions. We provide a balanced solution that addresses the trade-off by including user preference. Our simulation results indicate 5% to 18% improvement in base station power consumption and 13% to 25% improvement in UE power conservation chances. The provided solution also decreases the transmitted data in the network while preserving the user perceived quality of the video.

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 score0.613
Threshold uncertainty score0.834

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 head0.014
GPT teacher head0.225
Teacher spread0.212 · 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

Quick stats

Citations8
Published2011
Admission routes1
Has abstractyes

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