QoE for Mobile Clients with Segment-aware Rate Adaptation Algorithm (SARA) for DASH Video Streaming

Yarnagula, Hema Kumar; Juluri, Parikshit; Mehr, Sheyda Kiani; Tamarapalli, Venkatesh; Medhi, Deep

doi:10.1145/3311749

Cited by 14 publications

(8 citation statements)

References 32 publications

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“…Visual Quality. This metric indicates the average video resolution received by the video player, particularly when the streaming rate and quality level are dynamically adapted to the available bandwidth, such as in DASH (Dynamic Adaptive Streaming over HTTP) [31]. In our experiment, the two legitimate Web sites and several FLS Web sites (except NBA providers) provide HD video quality.…”

Section: Video Quality Of Service (Qos) Analysismentioning

confidence: 99%

An Empirical Measurement Study of Free Live Streaming Services

Keshvadi

Williamson

2021

Passive and Active Measurement

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Live streaming is one of the most popular Internet activities. Nowadays, there has been an increase in free live streaming (FLS) services that provide unauthorized broadcasting of live events, attracting millions of viewers. These opportunistic providers often have modest network infrastructures, and monetize their services through advertising and data analytics, which raises concerns about the performance, quality of experience, and user privacy when using these services. In this paper, we measure and analyze the behaviour of 20 FLS sports sites on Android smartphones, focusing on packet-level, video player, and privacy aspects. In addition, we compare FLS services with two legitimate online sports networks. Our measurement results show that FLS sites suffer from scalability issues during highly-popular events, deliver lower QoE than legitimate providers, and often use obscure and/or suspicious tracking services. Caution is thus advised when using FLS services.

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Section: Video Quality Of Service (Qos) Analysismentioning

confidence: 99%

An Empirical Measurement Study of Free Live Streaming Services

Keshvadi

Williamson

2021

Passive and Active Measurement

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“…Hoßfeld et al [22] discussed factors that influence a QoE model. Yarnagula et al [23] formulated a complex parametric QoE model over a number of metrics. De Vriendt et al [24] addressed the problem of how to assess QoE of an end user under the form of a prediction for the MOS.…”

Section: Related Workmentioning

confidence: 99%

“…Note that our work focuses on quantitative moving QoE models. On the other hand, subjective QoE, measured using Mean Opinion Score (MOS), is studied for video delivery [23,36]. It is also pointed out in [36] that subjective assessments are costly, time-consuming, and not scalable.…”

Section: Related Workmentioning

confidence: 99%

Moving QoE for monitoring DASH video streaming: models and a study of multiple mobile clients

Mehr

Jogalekar

Medhi

2021

J Internet Serv Appl

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Objective Quality of Experience (QoE) for Dynamic Adaptive Streaming over HTTP (DASH) video streaming has received considerable attention in recent years. While there are a number of objective QoE models, a limitation of the current models is that the QoE is provided after the entire video is delivered; also, the models are on a per client basis. For content service providers, QoE observed is important to monitor to understand ensemble performance during streaming such as for live events or concurrent streaming when multiple clients are streaming. For this purpose, we propose Moving QoE (MQoE, in short) models to measure QoE during periodically during video streaming for multiple simultaneous clients. Our first model MQoE_RF is a nonlinear model considering the bitrate gain and sensitivity from bitrate switching frequency. Our second model MQoE_SD is a linear model that focuses on capturing the standard deviation in the bitrate switching magnitude among segments along with the bitrate gain. We then study the effectiveness of both models in a multi-user mobile client environment, with the mobility patterns being based on traces from a train, a car, or a ferry. We implemented the study on the GENI testbed. Our study shows that our MQoE models are more accurate in capturing the QoE behavior during transmission than static QoE models. Furthermore, our MQoE_RF model captures the sensitivity due to bitrate switching frequency more effectively while MQoE_SD captures the sensitivity due to the magnitude of the bitrate switching. Either models are suitable for content service providers for monitoring video streaming based on their preference.

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“…This idea was initially considered by the authors of this paper in [20]. Likewise, the concept has been also considered in [21,22], where it is proposed an ABR algorithm called SARA that knows the segments size of the whole video in advance, at the expense of modifying the MPD, instead of getting the size in runtime. Also, the authors of [23] use the extension part of the MPD to include information of instant bitrates of each segment to perform a proposed QoE-based video adaptation method.…”

Section: State Of the Artmentioning

confidence: 99%

“…It is important to highlight that the ABR algorithm presented in this paper, unlike some aforementioned theoretical ABR algorithms which have been evaluated in simulation scenarios, has been implemented and tested in a real environment. In this sense, among the several ABR algorithms existing in the literature, in this paper, apart from ExoPlayer, we have selected Müller [14] and SARA [21,22] to carry out the evaluation. The reason is that the papers in which these algorithms are described, unlike most papers, provide enough detail to implement and integrate these ABR algorithms into a real player.…”

Section: State Of the Artmentioning

confidence: 99%

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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QoE for Mobile Clients with Segment-aware Rate Adaptation Algorithm (SARA) for DASH Video Streaming

Cited by 14 publications

References 32 publications

An Empirical Measurement Study of Free Live Streaming Services

An Empirical Measurement Study of Free Live Streaming Services

Moving QoE for monitoring DASH video streaming: models and a study of multiple mobile clients

Look ahead to improve QoE in DASH streaming

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