Cloud Services for Media and Entertainment - Processing, Playout and Distribution
Channel: SMPTE NY
Date: 2021-04-06

SMPTE NY Meeting January 27, 2021

Speakers/Panelists: 
Rick Phelps, Founder, Brklyn Media
Chris Ziemer, VP Strategic Accounts & Partnerships, Imagine Communications
Joe Ashba, Senior Solutions Architect, Imagine Communications
Alex Emmermann, Cloud Business Development, Telestream
Evan Statton, Principal Architect, Amazon Web Services (AWS)
Liam Morrison, Principal Architect, Machine Learning, Amazon Web Services (AWS)

Producer/Moderator – Ed DeLauter
Coproducers – Barry Grossman, Nathaniel Bonini
Tech – Wes Simpson

Selected Q+A:
Q: How do you budget due to the 'transactional' nature of AWS cloud microservices?
AWS: I’ve found the best way to handle this kind of thing is to build a calculator from scratch using whatever tool is best (I like Quip or Excel). Feel free to email me at stattone@amazon.com if you want to see specifically how I typically do it.  What I do is create a spreadsheet with all the pricing dimensions, then I plug in my quantities, and if you have an enterprise discount or private pricing arrangement, then discount accordingly. Be sure to track storage and egress costs along with the transcoding. Sometimes people go through this exercise and then conclude something or another only to find out later that there were options/optimizations/etc. So - we’re happy to help if you’d like.

Q: When you migrate TV infrastructure to the cloud what are the biggest problems that come up?
A: 1) Not understanding available services and architecting ineffectively or in a way that is less resilient or more expensive than necessary. 2) Expecting things to work exactly the same as they do on-prem.  Sometimes they do, sometimes they don’t. Important to do testing and not make assumptions, and ask for help if needed. 3) Misunderstanding the costs because each service has different pricing dimensions.

Q: How big are the variables between different workflow designs? For example, are all OTT VOD platform designs basically the same?
Brklyn: They are typically different.  The video specs may be the same but Artwork and metadata can also but unique.  You can see the specs for delivering to Netflix, iTunes, Apple TV, Prime Video Direct on the web and there are significant differences in them let alone all the various MVPDs

Q: When designing cloud workflows, what are the main causes of latency? When fighting latency what are the trade offs?
A1: It’s usually a balance between latency vs. reliability vs. cost.  For example, if you can increase reliability by using protocols that use retransmissions or segmentation (RIST/Zixi/SRT/HLS/DASH/etc). But this adds latency too (usually just single digit seconds though). Or, you can go the other way and use straight UDP which reduces your latency to half your ping time (low milliseconds usually), but then it’s unreliable. So if you want the highest reliability with the lowest possible latency, use 2022-7 where you send the same signal twice (100% overhead/redundancy), and then it’s statistically unlikely packets would go missing on two redundant paths. But this increases cost.
A2: Latency in a transport stream model with Versio is 4-6 seconds ground to cloud (if using mediaconnect) and then from the output of the Versio, it’s 4 seconds to generate the encode. For the master control operator, we have live pip previews in the UI to assuage concerns of live switching within the Versio environment, presenting a seamless operator experience.

Q: How long does it take to train the ML models for upconverting or transcoding?  Are those micro services or do you sin up instances and run the software that way?
AWS: It varies, training will likely take quite a while, but the inference is what i was showing, which is the upscale or frame interpolation. it can be done fairly quickly (triple digit milliseconds to low seconds), but would likely not be done in real time for some of these use cases (currently). Other methods are quicker. you may spin up dedicated instances if the GPU backed inferences are needed.

Q: Could a version of your technology be used for restoring old musical recordings?
AWS: Absolutely, its being done today, there is audio super resolution, which seeks to de-noise, cleanup or improve audio. AWS also has a generative ai platform for music with DeepComposer, which provides deep learning based accompaniment. https://arxiv.org/abs/1708.00853 - Audio Super Resolution using Neural Networks

Q: Will GPU be available in the cloud in the future?  If so, when?
AWS: GPU’s are available in the cloud generally, its both used in many of the higher level services, but also available as instances (P4,G4, P3 in AWS)

Q: Does cloud infrastructure "wear out" like physical infrastructure does?
A: Hardware definitely fails, but the way cloud infrastructure is designed you can elegantly recover. One example is when you restart an instance it can/will start on a completely different physical device.  https://aws.amazon.com/premiumsupport/knowledge-center/ec2-instance-retirement/