A Network-Layer QoE Model for YouTube Live in Wireless Networks

Jiménez, Luis Roberto; Solera, Marta; Toril, M.

doi:10.1109/access.2019.2918433

Cited by 22 publications

(12 citation statements)

References 33 publications

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“…Moreover, it demonstrates QoE across several operators [35]. In [36], authors model the QoE of YouTube Live service from packet-level data in a Wi-Fi network. Similarly, Oliver-Balsalobre et al [37] model key service performance indicators from TCP/IP metrics for video-streaming (YouTube) in a Wi-Fi network has been presented.…”

Section: Related Workmentioning

confidence: 99%

ERRANT: Realistic emulation of radio access networks

2020

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Mobile devices drastically changed how people use the Internet. We use smartphones to access a heterogeneous catalog of web services such as news, social networks, audio/video streaming. Differently from wired connections, mobile networks do not offer the same kind of performance stability yet. Thus, service providers have to handle different network scenarios, e.g., 3G or 4G, while promising good end-users' quality of experience (QoE). To ensure that QoE is adequate, it is necessary to thoroughly test applications with a wide range of possible network conditions. For this, network emulation is of vital importance as it allows a tester to run experiments with a wide range of network conditions. However, when it comes to mobile networks, the variety of technical characteristics, coupled with the opaque network configurations, makes realistic emulation a challenging task. Most of the freely available emulation tools rely on a simple emulation, offering limited variability performances for each network condition.In this paper, we propose ERRANT, EmulatoR of Radio Access NeTworks, an open-source tool that emulates mobile networks with a high level of realism, following a data-driven approach. We use a large-scale dataset composed of 100 k speed test measurements collected from 4 network operators in 2 countries. We create 32 different network profiles based on different countries, operators, radio access technologies, and signal qualities. For each profile, we obtain both typical behavior and variability for latency, download and upload bandwidth. We use the profiles to create models by means of the Kernel Density Estimation. Then, ERRANT employs the tc-netem Linux tool and the models for emulation. In this way, ERRANT offers realistic network emulation, in which both typical behavior and network variability are accurately recreated.We validate ERRANT models with an independent dataset of HTTP downloads performed on the same mobile networks as of the profiles. Results show the effectiveness of ERRANT in the emulation of real mobile networks in terms of average behavior and obtained variability. We also show the limitations of a simple emulation, and of other freeware approaches versus ERRANT. Finally, we show two practical use cases to demonstrate the benefits of a dynamic emulation in understanding the performance of web browsing and video streaming. To run new measurement campaigns and create new models, we provide guidelines along with the required open-source code.

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Section: Related Workmentioning

confidence: 99%

ERRANT: Realistic emulation of radio access networks

2020

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“…However, collecting subjective quality assessments can be long, tiredness and expensive. As a consequence, operators are changing legacy network management approaches, focused on network performance and QoS to a more modern approach focused on user opinion and quality of experience QoE [10]. Good network QoS parameters do not necessarily mean that the end user is satisfied with the provided service, since his satisfaction depends and on other factors.…”

Section: Qoe In Radio Network Optimisationmentioning

confidence: 99%

“…The video dataset contain subjective assessments that can be helpful to create QoS to QoE mapping model for special radio network service -video transmission. Nowadays, radio network services such as live streaming video using YouTube or Facebook depends a lot on subjective perception [10]. The great challenge for all researcher is to create optimal and universal model for QoS to QoE mapping for all services which operators provide.…”

Section: Qoe In Radio Network Optimisationmentioning

confidence: 99%

Analysis of the impact of network architecture on signal quality in LTE technology

et al. 2020

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In this paper, the impact of the network architecture on signal quality in the fourth generation of the public mobile network is analyzed. The analysis was performed using RSRP (Reference Signal Received Power), RSRQ (Reference Signal Received Quality), SINR (Signal to Interference plus Noise Ratio) and throughput parameters in indoor environment. The signal quality parameters were collected by measurement using TEMS Investigation and TEMS Pocket software. The measurements were carried out at the School of Electrical Engineering on the ground floor of the Technical Faculty building for the macro and micro cell scenario. It has been found that better signal quality is ensured in micro cells. Quality of the signal is also considered by the various services provided to the users.

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“…For example, a subjective QoE assessment for VoIP applications in a real wireless environment was carried out in [134]. While, authors in [135], [136], and [137], respectively, proposed a mapping scheme from SVC layers to DASH layers that can provide improved QoE, a QoE model for VR video transmission over wireless network through the relations between subjective QoE and latitude, and an analytical model to estimate the QoE for encrypted YouTube Live service from packet-level data collected in the interfaces of a wireless network.…”

Section: Quality Of Experience (Qoe)mentioning

confidence: 99%

Why is Multimedia Quality of Experience Assessment a Challenging Problem?

et al. 2019

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Quality of experience (QoE) assessment occupies a key role in various multimedia networks and applications. Recently, large efforts have been devoted to devise objective QoE metrics that correlate with perceived subjective measurements. Despite recent progress, limited success has been attained. In this paper, we provide some insights on why QoE assessment is so difficult by presenting few major issues as well as a general summary of quality/QoE formation and conception including human auditory and vision systems. Also, potential future research directions are described to discern the path forward. This is an academic and perspective article, which is hoped to complement existing studies and prompt interdisciplinary research. INDEX TERMS Audiovisual perception, data-driven analysis, multimedia quality, objective quality metric, quality of experience, quality of services (QoS).

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A Network-Layer QoE Model for YouTube Live in Wireless Networks

Cited by 22 publications

References 33 publications

ERRANT: Realistic emulation of radio access networks

ERRANT: Realistic emulation of radio access networks

Analysis of the impact of network architecture on signal quality in LTE technology

Why is Multimedia Quality of Experience Assessment a Challenging Problem?

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