Experimental Analysis of ViLTE Service

Callegari, Christian; Garroppo, Rosario G.; Giordano, Stefano; Labrozzo, Calogero Carlo; Procissi, Gregorio; Minissale, Giovanni; Topazzi, Simone

doi:10.1109/access.2018.2821701

Cited by 3 publications

(1 citation statement)

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“…Numerous works have investigated and evaluated various types of service over wireless networks. In [4], the authors evaluated the perceived quality of the ViLTE service by performing an experimental testbed realized at a mobile provider. Several network QoS parameters such as packet loss and packet delay variation are utilized to predict differential MOS of the ViLTE services.…”

Section: Introductionmentioning

confidence: 99%

Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks

El‐Saleh¹,

Alhammadi²,

Shayea³

et al. 2022

Computers, Materials &Amp; Continua

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Mobile broadband (MBB) networks are expanding rapidly to deliver higher data speeds. The fifth-generation cellular network promises enhanced-MBB with high-speed data rates, low power connectivity, and ultralow latency video streaming. However, existing cellular networks are unable to perform well due to high latency and low bandwidth, which degrades the performance of various applications. As a result, monitoring and evaluation of the performance of these network-supported services is critical. Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users. This paper proposes a Bayesian model to estimate the minimum opinion score (MOS) of video streaming services for any particular cellular network. The MOS is the most commonly used metric to assess the quality of experience. The proposed Bayesian model consists of several input data, namely, round-trip time, stalling load, and bite rates. It was examined and evaluated using several test data sizes with various performance metrics. Simulation results show the proposed Bayesian network achieved higher accuracy overall test data sizes than a neural network. The proposed Bayesian network obtained a remarkable overall accuracy of 90.36% and outperformed the neural network.

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Section: Introductionmentioning

confidence: 99%