Client intelligence for adaptive streaming solutions

Jarnikov, DS Dmitri; Özçelebi, Tanır

doi:10.1016/j.image.2011.03.003

Cited by 49 publications

(14 citation statements)

References 16 publications

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“…Several solutions for improving the adaptation mechanism in the player have been proposed in the literature [11][12] [13][14] [15]. However, these implementations are a trade-off between flexibility on the one hand, and conservativeness on the other.…”

Section: Has Proxymentioning

confidence: 99%

Modeling Stability and Bitrate of Network-Assisted HTTP Adaptive Streaming Players

Kleinrouweler

Cabrero

Mei

et al. 2015

2015 27th International Teletraffic Congress

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Abstract-Viewers using HTTP Adaptive Streaming (HAS) without sufficient bandwidth undergo frequent quality switches that hinder their watching experience. This situation, known as instability, is produced when HAS players are unable to accurately estimate the available bandwidth. Moreover, when several players stream over a bottleneck link, their individual adaptation techniques may result in an unfair share of the channel. These are two detrimental issues in HAS technology, which is otherwise very attractive. To overcome them, a group of solutions are proposed in the literature that can be classified as network-assisted HAS. Solving stability and fairness only in the player is difficult, because a player has a limited view of the network. Using information from network devices can help players in making better adaptation decisions. The contribution of this paper is three-fold. First, we describe our implementation in the form of an HTTP proxy server, and show that both stability and fairness are strongly improved. Second, we present an analytical model that allows to compute the number of changes in video quality and the bitrate of a video stream. Third, we validate the accuracy of the model by comparing the modelbased estimations for the number of changes in video quality and for the mean bitrate of a video stream, with results in a real implementation of our HAS assistant. The results show that the model-based results are highly accurate. As such, this model is useful in practice for planning video delivery networks that use in-network HAS assistants, and enables us to analyze the stability and the mean bitrate of HAS streams prior to real deployment.

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Section: Has Proxymentioning

confidence: 99%

Modeling Stability and Bitrate of Network-Assisted HTTP Adaptive Streaming Players

Kleinrouweler

Cabrero

Mei

et al. 2015

2015 27th International Teletraffic Congress

View full text Add to dashboard Cite

show abstract

“…However, partially due to the bursty nature of HAS traffic, it is difficult for the players to make accurate bandwidth estimations [3,7] . More sophisticated adaptation algorithms with better heuristics and conservative switching between video profiles can lower the number unnecessary quality switches and improve fairness [8][9][10][11][12] . However, fixing the problems only in the player remains difficult because players have a limited view on what occurs in the network.…”

Section: Introductionmentioning

confidence: 99%

A model for evaluating sharing policies for network-assisted HTTP adaptive streaming

et al. 2016

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a b s t r a c t HTTP adaptive streaming (HAS) has become the dominant technology for streaming video over the Internet. It gained popularity because of its ability to adapt the video quality to the current network conditions and other appealing properties such as usage of off-the-shelf HTTP servers and easy firewall traversal. However, when multiple HAS players share a bottleneck link for streaming, the individual adaptation techniques in the players have difficulties to maintain a stable bitrate and fairly share the network resources. HAS-assisting network elements can solve these performance problems and allow execution of advanced policies for sharing the available bandwidth. Nonetheless, testing and evaluating new sharing policies is costly and time consuming. This motivated us to formulate a model that allows to differentiate between groups of users depending on the type of user or device, and that can describe the mean bitrate of the video streams and how often this bitrate is expected to change during playout. To show how our model can be used, we demonstrate two applications of our model. Furthermore, we validate the model based results against results obtained using our streaming testbed and proxy server based HASassistant. The results show that our model is highly accurate for both the mean bitrate and the number of changes in video bitrate. As such, our model is a useful tool for network administrators and internet service providers for evaluating the performance of sharing policies and for managing and provisioning video delivery networks.

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“…An end-to-end application-layer rate-control mechanism [6] was proposed based on the receiver's buffer-starvation probabilities analyzed using a discrete-time Markov chain. The work in [7] discussed the client intelligence required for adaptive video streaming based on progressive download over HTTP, like in HTTP Live streaming [8] and in 3GPP adaptive HTTP streaming [9]. In this letter, we conceive a receiver-driven adaptive layer-switching scheme for optimizing scalable video transmissions in the context of a state-of-the-art network architecture and protocols.…”

Section: Introductionmentioning

confidence: 99%

“…In order to achieve this, we rely on instantaneous throughput measurements for formulating an estimation model for a QoS-constrained end-to-end equivalent bandwidth, which is applied to instruct the video source to appropriately configure the video layers for transmission. In comparison to previous works [6], [7], the advantage of such a measurement-driven scheme is that it can eliminate the need for a complex, yet potentially inaccurate model of the network traffic. Hence, it can be invoked where no accurate traffic model is available.…”

Section: Introductionmentioning

confidence: 99%

Receiver-Driven Adaptive Enhancement Layer Switching Algorithm for Scalable Video Transmission Over Link-adaptive Networks

Yang

Hanzo

2013

IEEE Signal Process. Lett.

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Abstract-A receiver-driven adaptive layer switching algorithm is proposed for adapting the video bitrate to match the achievable network throughput. It relies on a QoS-constrained equivalent bandwidth estimator employed at the receiver, which is used for triggering the adjustment of video layers at the video source. Simulations are conducted to illustrate its efficiency by showing that it is capable of accommodating different channel qualities without their prior knowledge.Index Terms-Quality-of-service (QoS), scalable video coding, video layer switching, wireless networks.

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Client intelligence for adaptive streaming solutions

Cited by 49 publications

References 16 publications

Modeling Stability and Bitrate of Network-Assisted HTTP Adaptive Streaming Players

Modeling Stability and Bitrate of Network-Assisted HTTP Adaptive Streaming Players

A model for evaluating sharing policies for network-assisted HTTP adaptive streaming

Receiver-Driven Adaptive Enhancement Layer Switching Algorithm for Scalable Video Transmission Over Link-adaptive Networks

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