Fine-grained scalable streaming from coarse-grained videos

Ni, Pengpeng; Eichhorn, Alexander; Griwodz, Carsten; Halvorsen, Pål

doi:10.1145/1542245.1542269

Cited by 30 publications

(30 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The adaptation algorithm may try to maximise the value of (4) by adjusting the play-out quality to given network conditions as frequently as it is possible. Such behaviour will result in rapid oscillations of video quality, what will be negatively perceived by users [38,39]. For this reason, we introduce the second measure which sums the bit-rate switches:…”

Section: Quality Measuresmentioning

confidence: 99%

Traffic prediction methods for quality improvement of adaptive video

Biernacki

2017

Multimedia Systems

View full text Add to dashboard Cite

During the past years, adaptive video based on HTTP has become very popular. Streaming of the adaptive video relies heavily on an estimation of end-to-end network throughput, which can be challenging especially in mobile networks, where the capacity highly fluctuates. In this work, we propose to predict the network throughput using its past measurements. As the analysis shows, the network throughput forms a long range-dependent process; thus, for the throughput prediction, we apply a fractional ARIMA process and artificial neural networks. Our approach does not require any modifications to the network infrastructure or the TCP stack. The predictions are performed for data traces obtained from measurements of throughput of a real mobile network. As the experiment shows, the obtained traffic models are able to enhance the performance of an adaptive streaming algorithm. Compared to the throughput predictors employed in contemporary systems dedicated to adaptive video streaming, the proposed technique obtains better results when taking into account effectiveness of network capacity utilisation and stability of video play-out.

show abstract

Section: Quality Measuresmentioning

confidence: 99%

Traffic prediction methods for quality improvement of adaptive video

Biernacki

2017

Multimedia Systems

View full text Add to dashboard Cite

show abstract

“…Nevertheless, due to their versatility and relative easiness of use, they are extensively applied for modelling and forecasting of network traffic, e.g. in [43] for Ethernet traffic, in [44] for traffic from websites, or in [22,23] for general Internet traffic. However, as it was mentioned, network traffic has LRD, therefore, in order to capture its properties and overcome ARIMA limitations, researches sometimes use FARIMA processes [24,25].…”

Section: Previous Workmentioning

confidence: 99%

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

Biernacki

2016

Multimed Tools Appl

View full text Add to dashboard Cite

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.

show abstract

“…Several similar preload approaches use (hierarchical, layered) scalable video codecs like scalable MPEG (SPEG) [4], Multiple Description Coding (MDC) [3] and the Scalable Video Coding (SVC) extension to H.264 [13] to adapt the quality by filling a prefetch window bottom-up, i.e., starting with the lowest quality and building up the layers until the window is played out [4,12]. These are popular approaches, but potential challenges are too frequent quality switches which may annoy the users [16,9], and buffer underruns due to connection outages.…”

Section: Related Workmentioning

confidence: 99%

“…Choosing the right video bitrate at the right time, and knowing how much to buffer, are non-trivial tasks. For example, the quality should preferably not change too often, nor should it change by too many levels in a single jump as it negatively affects the user's perceived quality [9,16]. Ideally, one should trade off quality for more buffering to hide connectivity gaps.…”

Section: Introductionmentioning

confidence: 99%

Bitrate and video quality planning for mobile streaming scenarios using a GPS-based bandwidth lookup service

Riiser¹,

Vigmostad²,

Griwodz

et al. 2011

2011 IEEE International Conference on Multimedia and Expo

Self Cite

View full text Add to dashboard Cite

One of the main challenges when streaming video to mobile devices is to handle fluctuating bandwidth and frequent network outages as the device is brought in and out of areas with network coverage. In this paper, we propose bitrate and video quality planning algorithms for mobile streaming scenarios using a GPS-based bandwidth-lookup service in order to reduce frequently changing video quality and the number of playout interruptions. Our real world experiments using an adaptive segmented HTTP video streaming system while travelling on popular commute routes indicate that the users' quality of experience is greatly increased.Index Terms-adaptive streaming, GPS-based bandwidth prediction, bitrate and quality planning

show abstract

Fine-grained scalable streaming from coarse-grained videos

Cited by 30 publications

References 9 publications

Traffic prediction methods for quality improvement of adaptive video

Traffic prediction methods for quality improvement of adaptive video

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

Bitrate and video quality planning for mobile streaming scenarios using a GPS-based bandwidth lookup service

Contact Info

Product

Resources

About