Towards simple and smooth rate adaption for VBR video in DASH

Zhou, Yanping; Duan, Yizhou; Sun, Jun; Guo, Zongming

doi:10.1109/vcip.2014.7051491

Cited by 20 publications

(22 citation statements)

References 7 publications

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“…In recent years, many heuristic adaptation methods for adaptive streaming have been developed (e.g., [5][6][7][8][9]14]). An extensive evaluation of typical adaptation methods has been carried out in [15].…”

Section: Related Workmentioning

confidence: 99%

“…For streaming VBR video, several adaptation methods have been proposed based on the sensibility of the receiver's buffer [6,16]. Dubin et al [16] propose an adaptation logic that supports its bandwidth estimation decisions based on the client buffer redundancy.…”

Section: Related Workmentioning

confidence: 99%

“…This method considers the fluctuations of mobile network without emphasizing the characteristics of bitrate fluctuation of VBR videos, which leads to the lack of smoothness. In [6], a partial-linear trend prediction model is developed to estimate the trend of client buffer level variation. The client will continue to have the current version for the next segment when the estimated buffer level has no significant change.…”

Section: Related Workmentioning

confidence: 99%

“…The channel bandwidth is simulated using DummyNet [28]. We use the Tokyo Olympic video from [6]. For the video, max = 9, and, for the bandwidth, = 10.…”

Section: Experiments Setupmentioning

confidence: 99%

“…The purpose of buffer-based methods is to maintain the stability of the buffer within a certain range to ensure continuous video playback. However, when the bandwidth is drastically reduced, the buffer-based methods may cause sudden change of bitrate [5][6][7][8]. Meanwhile, throughput-based methods adaptively decide version based on the estimated throughput.…”

Section: Introductionmentioning

confidence: 99%

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SDP-Based Quality Adaptation and Performance Prediction in Adaptive Streaming of VBR Videos

Nguyen

Ngoc

et al. 2017

Advances in Multimedia

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Recently, various adaptation methods have been proposed to cope with throughput fluctuations in HTTP adaptive streaming (HAS). However, these methods have mostly focused on constant bitrate (CBR) videos. Moreover, most of them are qualitative in the sense that performance metrics could only be obtained after a streaming session. In this paper, we propose a new adaptation method for streaming variable bitrate (VBR) videos using stochastic dynamic programming (SDP). With this approach, the system should have a probabilistic characterization along with the definition of a cost function that is minimized by a control strategy. Our solution is based on a new statistical model where the future streaming performance is directly related to the past bandwidth statistics. We develop mathematical models to predict and develop simulation models to measure the average performance of the adaptation policy. The experimental results show that the prediction models can provide accurate performance prediction which is useful in planning adaptation policy and that our proposed adaptation method outperforms the existing ones in terms of average quality and average quality switch.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…The channel bandwidth is simulated using DummyNet [28]. We use the Tokyo Olympic video from [6]. For the video, max = 9, and, for the bandwidth, = 10.…”

Section: Experiments Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SDP-Based Quality Adaptation and Performance Prediction in Adaptive Streaming of VBR Videos

Nguyen

Ngoc

et al. 2017

Advances in Multimedia

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An Efficient QoE-Aware HTTP Adaptive Streaming over Software Defined Networking

et al. 2020

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Look ahead to improve QoE in DASH streaming

Belda

Fez

Arce

et al. 2020

Multimed Tools Appl

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When a video is encoded with constant quality, the resulting bitstream will have variable bitrate due to the inherent nature of the video encoding process. This paper proposes a video Adaptive Bitrate Streaming (ABR) algorithm, called Look Ahead, which takes into account this bitrate variability in order to calculate, in real time, the appropriate quality level that minimizes the number of interruptions during the playback. The algorithm is based on the Dynamic Adaptive Streaming over HTTP (DASH) standard for on-demand video services. In fact, it has been implemented and integrated into ExoPlayer v2, the latest version of the library developed by Google to play DASH contents. The proposed algorithm is compared to the Müller and Segment Aware Rate Adaptation (SARA) algorithms as well as to the default ABR algorithm integrated into ExoPlayer. The comparison is carried out by using the most relevant parameters that affect the Quality of Experience (QoE) in video playback services, that is, number and duration of stalls, average quality of the video playback and number of representation switches.These parameters can be combined to define a QoE model. In this sense, this paper also proposes two new QoE models for the evaluation of ABR algorithms. One of them considers the bitrate of every segment of each representation, and the second is based on VMAF (Video Multimethod Assessment Fusion), a Video Quality Assessment (VQA) method developed by Netflix. The evaluations presented in the paper reflect: first, that Look Ahead outperforms the Müller, SARA and the ExoPlayer ABR algorithms in terms of number and duration of video playback stalls, with hardly decreasing the average video quality; and second, that the two QoE models proposed are more accurate than other similar models existing in the literature.

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Towards simple and smooth rate adaption for VBR video in DASH

Cited by 20 publications

References 7 publications

SDP-Based Quality Adaptation and Performance Prediction in Adaptive Streaming of VBR Videos

SDP-Based Quality Adaptation and Performance Prediction in Adaptive Streaming of VBR Videos

An Efficient QoE-Aware HTTP Adaptive Streaming over Software Defined Networking

Look ahead to improve QoE in DASH streaming

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