Multi-access Edge Computing video analytics of ITU-T P.1203 Quality of Experience for streaming monitoring in dense client cells

Viola, R.; Zorrilla, Mikel; Angueira, Pablo; Montalbán, Jon

doi:10.1007/s11042-022-12537-4

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Concerning media-specific monitoring in [13], a MEC proxy is proposed for monitoring all the traffic exchanged at RAN between the video players, media server, and Content Delivery Network (CDN). That MEC proxy collects metrics of the application layer (L7) in relation to the streaming session.…”

Section: Related Workmentioning

confidence: 99%

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Multi-Layer Monitoring at the Edge for Vehicular Video Streaming: Field Trials

Yeregui

Uriol

Viola

et al. 2023

2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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In an increasingly connected world, wireless networks' monitoring and characterization are of vital importance. Service and application providers need to have a detailed understanding of network performance to offer new solutions tailored to the needs of today's society. In the context of mobility, in-vehicle infotainment services are expected to stand out among other popular connected vehicle services, so it is essential that communication networks are able to satisfy the Quality of Service (QoS) and Quality of Experience (QoE) requirements needed for these type of services. This paper investigates a multi-layer network performance monitoring architecture at the edge providing QoS, QoE, and localization information for vehicular video streaming applications in realtime over 5G networks. In order to conduct field trials and show test results, Mobile Network Operators (MNOs)' 5G Standalone (SA) network and Multi-access Edge Computing (MEC) infrastructure are used to provide connectivity and edge computing resources to a vehicle equipped with a 5G modem.

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Section: Related Workmentioning

confidence: 99%

“…The introduction of a media proxy opens the possibility of tracking a player's activity and collecting information on its playback session. Moreover, collecting information from MPD and media segments allows for estimating the QoE of each user [13] .…”

Section: Multi-layer Monitoring Architecturementioning

confidence: 99%

Multi-Layer Monitoring at the Edge for Vehicular Video Streaming: Field Trials

Yeregui

Uriol

Viola

et al. 2023

2023 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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“…Effective pre-fetching algorithms optimize the delivery of video segments, ensuring a seamless streaming experience. Optimization in DASH includes monitoring and assessing quality of service (QoS) metrics to ensure high user quality of Experience (QoE) [28)]. These QoS metrics may include video quality (e.g., PSNR, SSIM), rebuffering ratio, start-up delay, smoothness, and stalling events.…”

mentioning

confidence: 99%

A Taxonomy for Deep Learning in Dynamic Adaptive Video Streaming Over HTTP

Khan¹

2023

IJMRA

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Deep Learning (DL) has become a fundamental technology in the field of Dynamic Adaptive Video Streaming over HTTP (DASH), enabling significant advancements in video streaming systems. This taxonomy presents a novel framework for categorizing and organizing the diverse applications and methodologies of DL in DASH. The taxonomy encompasses various aspects of DL, including video representation, quality of experience (QoE) estimation, bitrate adaptation, buffer management, content- and context-aware adaptation, and network optimization. By providing a comprehensive overview of DL in DASH, this taxonomy serves as a valuable resource for researchers and practitioners, facilitating a better understanding of the different DL techniques and their applications in enhancing video streaming performance and user experience.

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An optimized environment-adaptive computation offloading strategy for real-time cross-camera task in edge computing networks

Yang

Jiang

et al. 2023

Multimed Tools Appl

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Multi-access Edge Computing video analytics of ITU-T P.1203 Quality of Experience for streaming monitoring in dense client cells

Cited by 3 publications

References 24 publications

Multi-Layer Monitoring at the Edge for Vehicular Video Streaming: Field Trials

Multi-Layer Monitoring at the Edge for Vehicular Video Streaming: Field Trials

A Taxonomy for Deep Learning in Dynamic Adaptive Video Streaming Over HTTP

An optimized environment-adaptive computation offloading strategy for real-time cross-camera task in edge computing networks

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