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Record W2159018930 · doi:10.1109/icme.2005.1521479

A client-driven scalable cross-layer retransmission scheme for 3G video streaming

2005· article· en· W2159018930 on OpenAlexaff
Hao Liu, Wen Jun Zhang, Song Yu Yu, Jun Cai

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Network Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRetransmissionComputer scienceComputer networkNetwork packetScalabilityQuality of serviceReal-time computingWirelessApplication layerVideo qualityBandwidth (computing)FadingWireless networkLossy compressionChannel (broadcasting)TelecommunicationsSoftware deployment

Abstract

fetched live from OpenAlex

The wireless channel is time-varying where burst packet losses often occur during the fading or lossy handovers. In order to avoid unaccepted quality degradation of video streaming over 3G cellular networks, we propose and analyze a client-driven scalable cross-layer (CSC) retransmission scheme. Considering the perceptual importance of different video partitions under the real-time and bandwidth constraints, the proposed scheme uses the radio link-layer retransmission with priority to adapt conventional packet losses in wireless channels; furthermore, it uses the adaptive transport-layer retransmission to provide end-to-end quality-of-service (QoS) guarantees over cellular networks. The simulation experiments show that the proposed scheme can effectively improve the perceptual quality of 3G video streaming as compared to the traditional deadline-based scheme without the prioritized link-layer retransmission.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.265
Teacher spread0.253 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations6
Published2005
Admission routes1
Has abstractyes

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