Video: Key Elements of Live Stream Monitoring
Learn more about stream monitoring at Streaming Media's next event.
Watch the complete video of this panel, DT203A: Live Streaming: What Could Go Wrong?, in the Streaming Media Conference Video Portal.
Read the complete transcript of this clip:
Dan Murray: The first basic thing is, are all the assets there? Are they the right assets? What's the latency in grabbing that asset? I mean, is it quickly accessible or is there a long delay? But more than just, is the asset there, Tektronix believes you should really fully look and analyze all the content. What does the video look like, are the audio levels okay, is there closed captioning? What about ad insertion, and all the detailed analytics to determine, is everything fine inside of that asset?
Once you've put that in place, then it provides the diagnostics so if there is something going wrong, hopefully you found out before your customer, but if not, you've got the diagnostic tools to find it quickly.
If you can look at your streams at multiple points, then you have that kind of visibility to see where it went wrong and help with that diagnostics.
Compliance or maybe not as much compliance requirements on Cloud today but I bet it's coming. I think we've seen some closed-captioning requirements. If you have television on cable, it's closed captioned. It's supposed to be closed captioned as well on streaming.
Will there be similar things for audio and other compliance? One of our streaming customers said a top-three complaint is audio, and being able to tell if that happened in their network or from the source is important.
As they scale, more and more operators want to automate, so it’s important to have a good API and be able to do that. Ad insertion, whether it's a traditional ad insert or dynamic, diagnostics for that can be very difficult. Multiple vendors, multiples pieces and being able to do that.
Then just a full range of reports, either for business or technical reasons and things like setting thresholds because if you scale and you automate, you want to use by exception so if something goes wrong, you know it and you can go work on that.
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