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Record W2137242175 · doi:10.1109/icc.2011.5962785

A Seamless Quality-Driven Multi-Hop Data Delivery Scheme for Video Streaming in Urban VANET Scenarios

2011· article· en· W2137242175 on OpenAlexaff
Mahdi Asefi, Sandra Céspedes, Xuemin Shen, J.W. Mark

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceComputer networkVehicular ad hoc networkHandoverNetwork packetWireless ad hoc networkReal-time computingVideo streamingQuality of experienceQuality of serviceWirelessTelecommunications

Abstract

fetched live from OpenAlex

The inherent characteristics of vehicular networks, such as dynamic topology and high mobility, pose significant challenges for the deployment of delay-sensitive applications, e.g., video streaming, in urban Vehicular Ad-hoc Networks (VANET). In this paper, we propose an integrated network-layer scheme for seamless delivery of video packets in VANET. First, we propose a new quality-driven routing scheme for delivering video streams from a fixed network to a destination vehicle via multihop communications. The routing scheme aims to optimize the visual quality of the transmitted video frames by minimizing the distortion, the start-up delay, and the frequency of the streaming freezes. We further propose an efficient network mobility management scheme, which introduces a novel adaptation of Proxy Mobile IPv6 (PMIPv6) for multi-hop VANET scenarios, and incorporates a handover prediction mechanism. Numerical results are given to demonstrate that our integrated scheme can achieve a better performance for the video quality metrics, the handover delay and the signalling cost.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.089
GPT teacher head0.283
Teacher spread0.194 · 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 teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations28
Published2011
Admission routes1
Has abstractyes

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