Application flow control in YouTube video streams

Alcock, Shane; Nelson, Richard

doi:10.1145/1971162.1971166

Cited by 90 publications

(83 citation statements)

References 11 publications

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“…they are dependent on the type of a web browser or a mobile application, and the type of a container used for video streaming. These findings are in agreement with the results presented in [8], where the authors found that the character of ON-OFF cycles depends on the available bandwidth of a network. The cycles are observed when the end-to-end available bandwidth exceeds the transmitted video bit rate by a certain percent.…”

Section: Previous Worksupporting

confidence: 93%

“…However, as the HTTP server progressively sends (streams) the whole video content to the client and usually does not take into account how much of the data has been already sent in advance, an abundance of data can overwhelm the video player and lead to a large amount of unused bytes if a user interrupts the video play-out [8]. To avoid such an undesirable situation, the video file is divided into chunks of fixed length and the server pushes them sequentially to the client at a rate little higher than the video-bit rate of the transmitted content.…”

Section: Application Level Flow Controlmentioning

confidence: 99%

“…When AR (8) and MA (9) terms are added together in a single expression, we obtain a time series {x t } which follows an autoregressive moving average (ARMA) process of order (p, q), denoted as ARMA(p,q)…”

Section: Arima and Farima Modelsmentioning

confidence: 99%

“…On the other hand, we have works based on user traffic trace analysis including deep packet inspection, e.g. [8,[36][37][38]. Some works tried to investigate details of traffic characteristics, e.g.…”

Section: Previous Workmentioning

confidence: 99%

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Analysis and modelling of traffic produced by adaptive HTTP-based video

Biernacki

2016

Multimed Tools Appl

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The increase of HTTP-based video popularity causes that broadband and Internet service providers' links transmit mainly multimedia content. Network planning, traffic engineering or congestion control requires an understanding of the statistical properties of network traffic; therefore, it is desirable to investigate the characteristic of traffic traces generated by systems which employ adaptive bit-rate streaming. Our first contribution is an investigation of traffic originating from 120 client-server pairs, situated in an emulated content distribution network, and multiplexed onto a single network link. We show that the structure of the traffic is distinct from the structure generated by the first and second generation of HTTP video systems, and furthermore, not similar to the structure of general Internet traffic. The obtained traffic exhibits negative and positive correlations, anti-persistence, and its distribution function is skewed to the right. Our second contribution is an approximation of the traffic by ARIMA/FARIMA processes blue and artificial neural networks. As we show, the obtained traffic models are able to enhance the performance of an adaptive streaming algorithm.

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Section: Previous Worksupporting

confidence: 93%

Section: Application Level Flow Controlmentioning

confidence: 99%

Section: Arima and Farima Modelsmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

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Analysis and modelling of traffic produced by adaptive HTTP-based video

Biernacki

2016

Multimed Tools Appl

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“…Rao et al [2] survey strategies used for video streaming at both Youtube and Netflix and characterise the properties of interleaved block sending patterns used to pace streams. These patterns are also the subject of [27], in which the burstiness of Youtube traffic in particular is found to result in considerable losses over residential connections. A large portion of the traffic observed in the MAWI dataset originates from Hyper Text Transfer Protocol (HTTP) file sharing services, commonly referred to as One-click Hosting (OCH) websites [3].…”

Section: Related Workmentioning

confidence: 99%

On rate limitation mechanisms for TCP throughput: A longitudinal analysis

et al. 2017

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TCP remains the dominant transport protocol for Internet traffic. It is usually considered to have its sending rate covered by a sliding window congestion control mechanism. However, in addition to this normal congestion control, a number of other mechanisms limit TCP throughput. This paper analyzes the extent to which network, host and application settings define flow throughput over time and across autonomous systems. Our study draws on data from a longitudinal study spanning five years of passive traces collected from a single transit link. Mechanisms for this include limiting by application, interference with the TCP window control mechanism and artificial limitations on maximum window sizes by the operating system. This paper uses a large data set to assess the impact of each mechanism. We conclude that more than half of all heavy-hitter inbound traffic remains throttled by constraints beyond network capacity. For this data set, TCP congestion control is no longer the dominant mechanism that moderates throughput.

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Analysis and modelling of YouTube traffic

Ameigeiras

Ramos-Muñoz

Navarro-Ortiz

et al. 2012

Trans. Emerging Tel. Tech.

115

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YouTube currently accounts for a significant percentage of the Internet's global traffic. Hence, understanding the characteristics of the YouTube traffic generation pattern can provide a significant advantage in predicting user video quality and in enhancing network design. In this paper, we present a characterisation of the traffic generated by YouTube when accessed from a regular PC. On the basis of this characterisation, a YouTube server traffic generation model is proposed, which, for example, can be easily implemented in simulation tools. The derived characterisation and model are based on experimental evaluations of traffic generated by the application layer of YouTube servers. A YouTube server commences the download with an initial burst and later throttles down the generation rate. If the available bandwidth is reduced (e.g. in the presence of network congestion), the server behaves as if the data excess that cannot be transmitted because of the reduced bandwidth were accumulated at a server's buffer, which is later drained if the bandwidth availability is recovered. As we will show, the video clip encoding rate plays a relevant role in determining the traffic generation rate, and therefore, a cumulative density function for the most viewed video clips will be presented. The proposed traffic generation model was implemented in a YouTube emulation server, and the generated synthetic traffic traces were compared with downloads from the original YouTube server. The results show that the relative error between downloads from the emulation server and the original server does not exceed 6% for the 90% of the considered videos. Copyright © 2012 John Wiley & Sons, Ltd.

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Application flow control in YouTube video streams

Cited by 90 publications

References 11 publications

Analysis and modelling of traffic produced by adaptive HTTP-based video

Analysis and modelling of traffic produced by adaptive HTTP-based video

On rate limitation mechanisms for TCP throughput: A longitudinal analysis

Analysis and modelling of YouTube traffic

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