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Record W2017732869 · doi:10.1108/17415650680000052

A scalable multimedia streaming scheme with CBR‐transmission of VBR‐encoded videos over the internet

2006· article· en· W2017732869 on OpenAlexaff
Md. Humayun Kabir, Gholamali C. Shoja, Eric G. Manning

Bibliographic record

VenueInteractive Technology and Smart Education · 2006
Typearticle
Languageen
FieldComputer Science
TopicPeer-to-Peer Network Technologies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceReal Time Streaming ProtocolComputer networkScalabilityServerVariable bitrateThe InternetConstant bitrateBandwidth (computing)JitterQuality of serviceMultimediaOperating systemTelecommunications

Abstract

fetched live from OpenAlex

Streaming audio/video contents over the Internet requires large network bandwidth and timely delivery of media data. A streaming session is generally long and also needs a large I/O bandwidth at the streaming server. A streaming server, however, has limited network and I/O bandwidth. For this reason, a streaming server alone cannot scale a streaming service well. An entire audio/video media file often cannot be cached due to intellectual property right concerns of the content owners, security reasons, and also due to its large size. This makes a streaming service hard to scale using conventional proxy servers. Media file compression using variable‐bit‐rate (VBR) encoding is necessary to get constant quality video playback although it produces traffic bursts. Traffic bursts either waste network bandwidth or cause hiccups in the playback. Large network latency and jitter also cause long start‐up delay and unwanted pauses in the playback, respectively. In this paper, we propose a proxy based constant‐bit‐rate (CBR)‐transmission scheme for VBR‐encoded videos and a scalable streaming scheme that uses a CBRtransmission scheme to stream stored videos over the Internet. Our CBR‐streaming scheme allows a server to transmit a VBRencoded video at a constant bit rate, close to its mean encoding bit rate, and deals with the network latency and jitter issues efficiently in order to provide quick and hiccup free playback without caching an entire media file. Our scalable streaming scheme also allows many clients to share a server stream. We use prefix buffers at the proxy to cache the prefixes of popular videos, to minimize the start‐up delay and to enable near mean bit rate streaming from the server as well as from the proxy. We use smoothing buffers at the proxy not only to eliminate jitter and traffic burst effects but also to enable many clients to share the same server stream. We present simulation results to demonstrate the effectiveness of our streaming scheme.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.005
GPT teacher head0.242
Teacher spread0.237 · 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
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

Citations0
Published2006
Admission routes1
Has abstractyes

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