Automated Objective and Subjective Evaluation of HTTP Adaptive Streaming Systems

Timmerer, Christian; Zabrovskiy, Anatoliy; Begen, Ali C.

doi:10.1109/mipr.2018.00080

Cited by 14 publications

(16 citation statements)

References 27 publications

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“…The adaptation logic used in our evaluation is based on the one used by the DASH reference client, dash.js. As it has been demonstrated in the literature [37], the simple design of the dash.js adaptation logic performs better than other more sophisticated adaptation logics.…”

Section: Evaluation Setupmentioning

confidence: 70%

Adaptive Video Streaming With Network Coding Enabled Named Data Networking

Saltarin

Bourtsoulatze

Thomos

et al. 2017

IEEE Trans. Multimedia

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Abstract-The fast and huge increase of Internet traffic motivates the development of new communication methods that can deal with the growing volume of data traffic. To this aim, Named Data Networking (NDN) has been proposed as a future Internet architecture that enables ubiquitous in-network caching and naturally supports multi-path data delivery. Particular attention has been given to using Dynamic Adaptive Streaming over HTTP (DASH) to enable video streaming in NDN as in both schemes data transmission is triggered and controlled by the clients. However, state-of-the-art works do not consider the multipath capabilities of NDN and the potential improvements that multipath communication brings, such as increased throughput and reliability, that are fundamental for video streaming systems. In this paper, we present a novel architecture for dynamic adaptive streaming over network coding enabled NDN. In comparison to previous works proposing dynamic adaptive streaming over NDN, our architecture exploits network coding to efficiently use the multiple paths connecting the clients to the sources. Moreover, our architecture enables efficient multi-source video streaming and improves resiliency to Data packet losses. The experimental evaluation shows that our architecture leads to reduced data traffic load on the sources, increased cache-hit rate at the innetwork caches and faster adaptation of the requested video quality by the clients. The performance gains are verified through simulations in a Netflix-like scenario.

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Section: Evaluation Setupmentioning

confidence: 70%

Adaptive Video Streaming With Network Coding Enabled Named Data Networking

Saltarin

Bourtsoulatze

Thomos

et al. 2017

IEEE Trans. Multimedia

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“…Still, they do not provide a source code level analysis of familiar Dynamic Adaptive Streaming over HTTP (DASH) players and support for popular commercial streaming players. In [59], same authors proposed an end-to-end QoE evaluation to collect and analyze objectively (AdViSE) and subjectively (Web-based subjective evaluation platform (WESP) [60]) the streaming performance metrics (e.g., start-up time, stalls, quality [62], to anticipate rebuffering events from encrypted video streaming traffic in real-time. This approach, after it subdivides the video streaming session into a series of time slots, that have the same length.…”

Section: B Objective Assessmentmentioning

confidence: 99%

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

et al. 2020

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Over the last few years, the evolution of network and user handsets' technologies, have challenged the telecom industry and the Internet ecosystem. Especially, the unprecedented progress of multimedia streaming services like YouTube, Vimeo and DailyMotion resulted in an impressive demand growth and a significant need of Quality of Service (QoS) (e.g., high data rate, low latency/jitter, etc.). Mainly, numerous difficulties are to be considered while delivering a specific service, such as a strict QoS, human-centric features, massive number of devices, heterogeneous devices and networks, and uncontrollable environments. Thenceforth, the concept of Quality of Experience (QoE) is gaining visibility, and tremendous research efforts have been spent on improving and/or delivering reliable and addedvalue services, at a high user experience. In this paper, we present the importance of QoE in wireless and mobile networks (4G, 5G, and beyond), by providing standard definitions and the most important measurement methods developed. Moreover, we exhibit notable enhancements and controlling approaches proposed by researchers to meet the user expectation in terms of service experience.

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“…There is also an end-end analysis of Yahoo's video streaming platform using data from the provider [8]. Several comparisons and analysis of academic ABR algorithms [29,26,24] have also been published, including within each of the several new proposals mentioned above. In particular, [24] compares three reference ABR implementations, showing that the configuration of various parameters has a substantial impact on their performance.…”

Section: Related Workmentioning

confidence: 99%

Understanding Video Streaming Algorithms in the Wild

Licciardello

Grüner

Singla

2020

Passive and Active Measurement

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While video streaming algorithms are a hot research area, with interesting new approaches proposed every few months, little is known about the behavior of the streaming algorithms deployed across large online streaming platforms that account for a substantial fraction of Internet traffic. We thus study adaptive bitrate streaming algorithms in use at 10 such video platforms with diverse target audiences. We collect traces of each video player's response to controlled variations in network bandwidth, and examine the algorithmic behavior: how risk averse is an algorithm in terms of target buffer; how long does it takes to reach a stable state after startup; how reactive is it in attempting to match bandwidth versus operating stably; how efficiently does it use the available network bandwidth; etc. We find that deployed algorithms exhibit a wide spectrum of behaviors across these axes, indicating the lack of a consensus one-size-fitsall solution. We also find evidence that most deployed algorithms are tuned towards stable behavior rather than fast adaptation to bandwidth variations, some are tuned towards a visual perception metric rather than a bitrate-based metric, and many leave a surprisingly large amount of the available bandwidth unused.

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Automated Objective and Subjective Evaluation of HTTP Adaptive Streaming Systems

Cited by 14 publications

References 27 publications

Adaptive Video Streaming With Network Coding Enabled Named Data Networking

Adaptive Video Streaming With Network Coding Enabled Named Data Networking

Quality of Experience for Streaming Services: Measurements, Challenges and Insights

Understanding Video Streaming Algorithms in the Wild

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