
Spirent Umetrix® Video | HD Streaming
Assuring Superior Quality of Experience for Streaming Video Services
Mobile is Now a Video Distribution Business
Mobile data demand is exponentially growing, with video as the main driver. According to Cisco’s Visual Networking
Index, 78% of the world’s mobile data trafc will be video by 2021.
1
The ecosystem for video content delivery is
expanding, too, across wireless carriers, device and chipset manufacturers, and streaming service providers, who
each have their own specialized needs to address:
• Global carriers are at the center of delivering streaming video content as strategically important to their business,
yet at the same time, must balance network optimization and Quality of Experience (QoE)
• Handset and chipset manufacturers must deliver devices optimized for video delivery, while specialty device
manufacturers are actively creating new video devices and services
• Streaming service providers need to deliver apps that perform across a wide variety of devices and carrier
networks
Video delivery is complicated, and as organizations
implement their video strategies, there are a myriad
of challenges and questions that arise. These
questions, and many others, can be answered through
comprehensive testing methods, but there are several
different ways to measure video performance. Rather
than analyzing packets or frames for diagnostic
testing, many performance evaluation methods use
pixel comparisons of the source versus the delivered
video to determine overall quality. This is a common
standardized method, but unfortunately it is not
applicable to most Over-the-Top (OTT) streaming
applications. Now there’s something new.
Spirent has developed a leading-edge methodology
to evaluate video performance without the need for
a reference source. The Umetrix® Video solution can
“view pixels like a person” and score QoE according to
a Video Mean Opinion Score (VMOS), as if hundreds
of human viewers were watching and rating overall
performance.
Umetrix Video supports any video service (e.g., mobile, home, 5G applications), analyzes the video content itself to
detect artifacts, and performs scoring without prior view of the original video. This analysis is via Spirent’s content-
trained non-reference algorithm, which uses machine learning on thousands of sample videos to understand the
variations in different types of content (sports, drama, animation). Content training is based on de facto industry
standards that correlate to human perceptual scoring. The result: faster and less expensive repeatable design
validation, regression testing, and competitive benchmarking.
Why does my
video chat service
have trouble in
one market?
How does my
OTT service
look over
mobile
networks?
How do I look
against my
competitor?
How does this
video service look
on my device vs.
other devices?
Is my new
strategic
streaming service
ready to launch?
1
Cisco Visual Networking Index: Global Mobile Data Trafc Forecast Update, 2016–2021 White Paper
Applications:
OTT video streaming | OTT video chat | IR.94
Non-commercial live streaming
Mission-Critical Video (MCVideo) for PS-LTE