PPPXoE - Adaptive Data Link Protocole for High-Speed Packet Networks

Ivchenko, Alexander; Antiufrieva, Liubov; Kononyuk, Pavel; Dvorkovich, Alexander V.

doi:10.1007/978-3-030-36625-4_15

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…In the original paper [7], this point was described without details; only the standard set of Linux utilities was specified traffic control and qdisc. In our other study [48], we encountered incorrect traffic control behavior when introducing packet loss -with 10% packet loss set, we had instantaneous burst losses of up to 90%. Some studies say that it is necessary to increase the duration of the session from 10 to 30 seconds [49].…”

Section: Discussionmentioning

confidence: 96%

“…Unlike UDP-based [4]- [5] data transfer, where video and audio packets are allowed to be lost and playback is performed using the existed data and damaged data decoder strategies, TCP guarantees data delivery by means of channel control and retransmissions of lost and damaged packets. However, this leads to other problems [48]: with insufficient bandwidth (below the video transmission speed) or/and packets losses, the playback buffer is depleted and stalling events occur. In addition, there is congestion control.…”

Section: Introductionmentioning

confidence: 99%

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Study on the Assessment of the Quality of Experience of Streaming Video

Ивченко

Kononyuk

Dvorkovich

et al. 2020

2020 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO)

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Dynamic adaptive streaming over HTTP provides the work of most multimedia services, however, the nature of this technology further complicates the assessment of the QoE (Quality of Experience). In this paper, the influence of various objective factors on the subjective estimation of the QoE of streaming video is studied. The paper presents standard and handcrafted features, shows their correlation and p-Value of significance. VQA (Video Quality Assessment) models based on regression and gradient boosting with SRCC reaching up to 0.9647 on the validation subsample are proposed. The proposed regression models are adapted for applied applications (both with and without a reference video); the Gradient Boosting Regressor model is perspective for further improvement of the quality estimation model. We take SQoE-III database, so far the largest and most realistic of its kind. The VQA (video quality assessment) models are available at https://github.com/AleksandrIvchenko/QoE-assesment

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%