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Record W2143576893 · doi:10.1109/vtc.2002.1002760

Rate-adaptive transmission of H.263 video for multicode DS/CDMA cellular systems in multipath fading

2003· article· en· W2143576893 on OpenAlexaff
C.-D. Iskander, P. Takis Mathiopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicWireless Communication Networks Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceReal-time computingVideo qualityFadingMultipath propagationCode division multiple accessTransmission (telecommunications)SmoothingBit error rateRake receiverDecoding methodsCodecComputer networkAlgorithmComputer hardwareTelecommunicationsChannel (broadcasting)Computer vision

Abstract

fetched live from OpenAlex

The variable bit rate nature of compressed video remains a challenge to the transmission of real-time video over mobile CDMA cellular networks. Indeed, H.263 and MPEG-4 video bitstreams are characterized. by high peak rates and frequent rate variations, which are difficult to support in 2.5G and 3G mobile networks. In this paper, we propose a simple scheme which minimizes the peak transmission rate and its variance, which leads to a lower average bit-error rate (BER) and thus a higher received video quality. By introducing a small delay in the decoding process, the video output stream can be buffered at the video decoder end. We propose an algorithm which uses this buffering capability to perform real-time smoothing of the video stream. The transmitter then varies the number of codes assigned to the stream in order to support the remaining smaller rate variations. We simulate an end-to-end video communication system for the IS-95B uplink. It consists of an H.263 codec, an H.223 multiplexer and the physical layer components of the IS-95B standard. The transmitted signal is subject to multipath fading and multiple-access interference, obtained by simulating all the other users in the cell. Our joint smoothing/rate adaptation algorithm leads to a significant gain in the video quality scheme, as compared to previous approaches.

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)0.002
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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.872
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.052
GPT teacher head0.295
Teacher spread0.242 · 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.

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

Citations3
Published2003
Admission routes1
Has abstractyes

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