Detecting Period and Burst Durations in Video Streaming by Means of Active Probing

Villa, Bjørn Jonny; Heegaard, Poul E.

doi:10.7763/ijcce.2013.v2.227

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…However, active probing methods may be less desirable in media streaming applications as these methods introduce additional traffic to the network (which already is a scarce resource in streaming) [37]. They are often not well-adapted to handle streaming traffic's periodic and bursty nature, either [173]. To mitigate these issues, [104] designed an active probing algorithm that probes using incremental data rate (instead of piggyback traffic) that backs off when network congestion is detected.…”

Section: Media Over Quic (Moq)mentioning

confidence: 99%

Catching the Moment With LoL$^+$ in Twitch-Like Low-Latency Live Streaming Platforms

Bentaleb

Akcay

Lim

et al. 2022

IEEE Trans. Multimedia

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Our earlier Low-on-Latency (dubbed as LoL) solution offered an accurate bandwidth prediction and rate adaptation algorithm tailored for live streaming applications that targeted an end-to-end latency of up to two seconds. While LoL was a significant step forward in multi-bitrate low-latency live streaming, further experimentation and testing showed that there was room for improvement in three areas. First, LoL used hardcoded parameters computed from an offline training process in the rate adaptation algorithm and this was seen as a significant barrier in LoL's wide deployment. Second, LoL's objective was to maximize a collective QoE function. Yet, certain use cases have specific objectives besides the singular QoE and this had to be accommodated. Third, the adaptive playback speed control failed to produce satisfying results in some scenarios. Our goal in this paper is to address these areas and make LoL sufficiently robust to deploy. We refer to the enhanced solution as LoL + , which has been integrated to the official dash.js player in v3.2.0.

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Section: Media Over Quic (Moq)mentioning

confidence: 99%

Catching the Moment With LoL$^+$ in Twitch-Like Low-Latency Live Streaming Platforms

Bentaleb

Akcay

Lim

et al. 2022

IEEE Trans. Multimedia

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“…However, in this work the probes can be programmed to measure the performance of the connection as measured at the destination(s) for specific configurations of packet trains that follow the expected traffic. In this regard, the authors in [26] used specific packet trains to measure different scenarios like network congestion and daily variations. However, those configurations are not related to real traffic conditions.…”

Section: Modeling and Assessing Connectivity Servicesmentioning

confidence: 99%

Modeling and Assessing Connectivity Services Performance in a Sandbox Domain

Ruiz

Tabatabaeimehr

et al. 2020

J. Lightwave Technol.

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The automation of Network Services (NS) consisting of virtual functions connected through a multilayer packet-overoptical network requires predictable Quality of Service (QoS) performance, measured in terms of throughput and latency, to allow making proactive decisions. QoS is typically guaranteed by overprovisioning capacity dedicated to the NS, which increases costs for customers and network operators, especially when the traffic generated by the users and/or the virtual functions highly varies over the time. This paper presents the PILOT methodology for modeling the performance of connectivity services during commissioning testing in terms of throughput and latency. Benefits are double: first, an accurate perconnection model allows operators to better operate their networks and reduce the need for overprovisioning; and second, customers can tune their applications to the performance characteristics of the connectivity. PILOT runs in a sandbox domain and constructs a scenario where an efficient traffic flow simulation environment, based on the CURSA-SQ model, is used to generate large amounts of data for Machine Learning (ML) model training and validation. The simulation scenario is tuned using real measurements of the connection (including throughput and latency) obtained from a set of active probes in the operator network. PILOT has been experimentally validated on a distributed testbed connecting UPC and Telefónica premises.

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“…Nevertheless, the amount of work focusing on traffic characteristics generated by adaptive video systems and its impact on video quality is quite limited. Some of the works tried to investigate details of traffic characteristics, for example in [30] the authors the examine the periodic behaviour of traffic generated by Microsoft Smooth Streaming and Netflix. In [31] the authors provide formulas for average intensity of network traffic and its variability.…”

Section: Related Workmentioning

confidence: 99%

Improving Video Quality by Diversification of Adaptive Streaming Strategies

2016

KSII TIIS

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Users quite often experience volatile channel conditions which negatively influence multimedia transmission. HTTP adaptive streaming has emerged as a new promising technology where the video quality can be adjusted to variable network conditions. Nevertheless, the new technology does not remain without drawbacks. As it has been observed, multiple video players sharing the same network link have often problems with achieving good efficiency and stability of play-out due to a mutual interference and competition among video players. Our investigation indicates that there may be another cause for under-performance of the streamed video. In an emulated environment, we implemented three algorithms of adaptive video play-out based on bandwidth or buffer assessment. As we show, traffic generated by players employing the same or similar play-out strategies is positively correlated and synchronised (clustered), whereas traffic originated from different play-out strategies shows negative or no correlations. However, when some of the parameters of the play-out strategies are randomised, the correlation and synchronisation diminish what has a positive impact on the smoothness of the traffic and on the video quality perceived by end users. Our research shows that non-correlated traffic flows generated by play-out strategies improve efficiency and stability of streamed adaptive video.

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Detecting Period and Burst Durations in Video Streaming by Means of Active Probing

Cited by 6 publications

References 21 publications

Catching the Moment With LoL$^+$ in Twitch-Like Low-Latency Live Streaming Platforms

Catching the Moment With LoL$^+$ in Twitch-Like Low-Latency Live Streaming Platforms

Modeling and Assessing Connectivity Services Performance in a Sandbox Domain

Improving Video Quality by Diversification of Adaptive Streaming Strategies

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