At Prime Video, we offer thousands of live streams to our customers. To ensure that customers seamlessly receive content, Prime Video set up a tool to monitor every stream viewed by customers. This tool allows us to automatically identify perceptual quality issues (for example, block corruption or audio/video sync problems) and trigger a process to fix them.
Our Video Quality Analysis (VQA) team at Prime Video already owned a tool for audio/video quality inspection, but we never intended nor designed it to run at high scale (our target was to monitor thousands of concurrent streams and grow that number over time). While onboarding more streams to the service, we noticed that running the infrastructure at a high scale was very expensive. We also noticed scaling bottlenecks that prevented us from monitoring thousands of streams. So, we took a step back and revisited the architecture of the existing service, focusing on the cost and scaling bottlenecks.
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The initial version of our service consisted of distributed components that were orchestrated by AWS Step Functions. The two most expensive operations in terms of cost were the orchestration workflow and when data passed between distributed components. To address this, we moved all components into a single process to keep the data transfer within the process memory, which also simplified the orchestration logic. Because we compiled all the operations into a single process, we could rely on scalable Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic Container Service (Amazon ECS) instances for the deployment.
Our service consists of three major components. The media converter converts input audio/video streams to frames or decrypted audio buffers that are sent to detectors. Defect detectors execute algorithms that analyze frames and audio buffers in real-time looking for defects (such as video freeze, block corruption, or audio/video synchronization problems) and send real-time notifications whenever a defect is found. For more information about this topic, see our How Prime Video uses machine learning to ensure video quality article. The third component provides orchestration that controls the flow in the service.
We designed our initial solution as a distributed system using serverless components (for example, AWS Step Functions or AWS Lambda), which was a good choice for building the service quickly. In theory, this would allow us to scale each service component independently. However, the way we used some components caused us to hit a hard scaling limit at around 5% of the expected load. Also, the overall cost of all the building blocks was too high to accept the solution at a large scale.
The main scaling bottleneck in the architecture was the orchestration management that was implemented using AWS Step Functions. Our service performed multiple state transitions for every second of the stream, so we quickly reached account limits. Besides that, AWS Step Functions charges users per state transition.
The second cost problem we discovered was about the way we were passing video frames (images) around different components. To reduce computationally expensive video conversion jobs, we built a microservice that splits videos into frames and temporarily uploads images to an Amazon Simple Storage Service (Amazon S3) bucket. Defect detectors (where each of them also runs as a separate microservice) then download images and processed it concurrently using AWS Lambda. However, the high number of Tier-1 calls to the S3 bucket was expensive.
To address the bottlenecks, we initially considered fixing problems separately to reduce cost and increase scaling capabilities. We experimented and took a bold decision: we decided to rearchitect our infrastructure.
Conceptually, the high-level architecture remained the same. We still have exactly the same components as we had in the initial design (media conversion, detectors, or orchestration). This allowed us to reuse a lot of code and quickly migrate to a new architecture.
In the initial design, we could scale several detectors horizontally, as each of them ran as a separate microservice (so adding a new detector required creating a new microservice and plug it in to the orchestration). However, in our new approach the number of detectors only scale vertically because they all run within the same instance. Our team regularly adds more detectors to the service and we already exceeded the capacity of a single instance. To overcome this problem, we cloned the service multiple times, parametrizing each copy with a different subset of detectors. We also implemented a lightweight orchestration layer to distribute customer requests.
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Many people can be involved in the production of a video and not all need to be listed in the citation. To clarify what role the person has in the production, precede each name (or each group of names, if more than one person performed the same function) with a description of the role. MLA style emphasizes finding this information whenever possible in the video itself, such as in the opening credits. Some common contributor roles that can be included in the citation are
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 Note: Typically films, television episodes, and other performances have many contributors. After the title, list the contributors most relevant to your project. Most common contributors listed include directors, creators, and performers.
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Software Engineer by trade so I'm willing to try more technical solutions if need be. Anyone experience this before and have any ideas for a fix? Also, I asked this question on the Amazon support forum and they suggested this is likely a Roku issue and to ask here.
Before we proceed, please be informed that this is a channel feature and it is highly recommended to directly raise your concern to the channel provider themselves as they are the ones who provided and maintained their channel on the Roku streaming platform.
If the issue persists on one channel after attempting the troubleshooting steps and videos from other channels play fine, contact the channel provider's customer support team to report the issue and get help. Channels on Roku are maintained by the channel developers themselves. In this case, there's likely an issue within that specific channel that needs to be addressed with an update from them. 152ee80cbc
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