A VLC media player plugin enabling dynamic adaptive streaming over HTTP

Müller, Christopher; Timmerer, Christian

doi:10.1145/2072298.2072429

Cited by 81 publications

(32 citation statements)

References 3 publications

(1 reference statement)

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“…12, we placed six video files, acquired from [63] and encoded in 300, 600, 1200, 2500 and 4400 kbit/s. These video files were streamed to the VLC media player with the embedded DASH plug-in [36]. Both the player and the plug-in have an open-source code, thus, it is possible to manipulate or completely change the adaptation logic without affecting the other components.…”

Section: Performance Of the Play-out Algorithmsmentioning

confidence: 99%

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Traffic prediction methods for quality improvement of adaptive video

Biernacki

2017

Multimedia Systems

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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.

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Section: Performance Of the Play-out Algorithmsmentioning

confidence: 99%

“…The idea of the above described algorithm is illustrated in Fig. 2, and we implemented it in the open-source software presented in [36].…”

mentioning

confidence: 99%

Traffic prediction methods for quality improvement of adaptive video

Biernacki

2017

Multimedia Systems

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“…In such a scenario, the video players compete among themselves for the available bandwidth needed to transfer video chunks of variable size [41,42]. The competition introduces additional video bit-rate oscillations, which translate into a higher probability of a buffer under-run of a video player and, consequently, video re-buffering.…”

Section: Previous Workmentioning

confidence: 99%

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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“…We extended the ICN architecture detailed in [13], [14] and interfaced it to the open source DASH client [18]. We configured the network setup on a single Linux machine: an Open vSwitch connecting four virtual machines (VMs).…”

Section: B Effect Of Caching On Rate Adaptationmentioning

confidence: 99%

On the Interaction of Adaptive Video Streaming with Content-Centric Networking

Grandl

Westphal

2013

2013 20th International Packet Video Workshop

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Abstract-Two main trends in today's internet are of major interest for video streaming services: most content delivery platforms coincide towards using adaptive video streaming over HTTP and new network architectures allowing caching at intermediate points within the network. We investigate one of the most popular streaming service in terms of rate adaptation and opportunistic caching. Our experimental study shows that the streaming client's rate selection trajectory, i.e., the set of selected segments of varied bit rates which constitute a complete video, is not repetitive across separate downloads. Also, the involvement of caching could lead to frequent alternation between cache and server when serving back client's requests for video segments. These observations warrant cautions for rate adaption algorithm design and trigger our analysis to characterize the performance of in-network caching for HTTP streaming. Our analytic results show: (i) a significant degradation of cache hit rate for adaptive streaming, with a typical file popularity distribution in nowadays internet; (ii) as a result of the (usually) higher throughput at the client-cache connection compared to client-server one, cacheserver oscillations due to misjudgments of the rate adaptation algorithm occur. Finally, we introduce DASH-INC, a framework for improved video streaming in caching networks including transcoding and multiple throughput estimation.

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A VLC media player plugin enabling dynamic adaptive streaming over HTTP

Cited by 81 publications

References 3 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

On the Interaction of Adaptive Video Streaming with Content-Centric Networking

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