Install

Media Replay Engine (MRE) is a framework for building automated video clipping and replay (highlight) generation pipelines using AWS services for live and video-on-demand (VOD) content. With MRE, you can focus on building the business logic around video clipping without worrying about pipeline orchestration, data movement and persistence.

MRE supports the following features:

Catchup Replay generation
After event Replay generation
Integration with MediaLive for processing live or VOD content
Event and Replay video export in MP4 and HLS formats
Event and Replay data export in EDL and JSON formats

This repository contains the core MRE Framework which is a set of secure REST APIs that you can interact with directly. It also contains the MRE Frontend application built using React if you are someone who prefers the ease-of-use of a graphical user interface (GUI) to interact with the APIs.

To get a head start in building automated video clipping pipelines using the MRE framework, check out the MRE Samples repository which contains sample Plugins, Profiles and ML Model Notebooks for performing feature detection for clip selection and prioritization.

Install

Prerequisites

python >= 3.8
aws-cli
aws-cdk >= 2.24.1
docker
node >= 14.15.0
npm >= 5.6
git

NOTE: If using an AWS Cloud9 environment to install MRE:
- Resize the EBS volume to at least 15 GB by following this guide.
- Ensure the installed version of Python is 3.8 or newer.

Greenfield Deployment

Greenfield deployment of MRE is suitable for customers who are looking to install from scratch.

Run the following commands to build and deploy MRE. Be sure to define values for REGION and VERSION first.

REGION=[specify the AWS region. For example, us-east-1]
VERSION=2.7.0
git clone https://github.com/awslabs/aws-media-replay-engine
cd aws-media-replay-engine
cd deployment
./build-and-deploy.sh --enable-ssm-high-throughput --version $VERSION --region $REGION [--profile <aws-profile>]

Upgrading

MRE Backend

In order to upgrade MRE Backend (StepFunctions, Lambda, API Gateway, EventBridge Rules, etc.), run the following commands. Be sure to define values for REGION and VERSION first.

REGION=[specify the AWS region. For example, us-east-1]
VERSION=2.7.0
git clone https://github.com/awslabs/aws-media-replay-engine
cd aws-media-replay-engine
cd deployment
./build-and-deploy.sh --disable-ui --version $VERSION --region $REGION [--profile <aws-profile>]

MRE Frontend

MRE uses AWS Amplify which offers a fully managed CI/CD and hosting service to deploy frontend apps on AWS by connecting to a Git repository such as AWS CodeCommit. As a part of MRE Greenfield deployment, a repository named mre-frontend is already created in AWS CodeCommit.

To upgrade MRE Frontend to the latest version, follow the below steps. Be sure to define a value for REGION which is typically where MRE Frontend is already deployed.

After cloning the repo from GitHub, navigate to aws-media-replay-engine/source/frontend/cdk folder.
Run the following commands to update the Amplify Environment variables and/or Custom headers.
```
 python3 -m venv venv
 source venv/bin/activate
 pip install -r requirements.txt
 python3 init-amplify.py $REGION Update [$AWS_PROFILE]
```
NOTE: The warning message about redeploying the frontend application after running the above command can be ignored as we will be doing that shortly.
Navigate to aws-media-replay-engine/source/frontend folder and clone the existing mre-frontend repository from AWS CodeCommit.
Copy all the files and subfolders (except node_modules/ and cdk/ subfolders) from aws-media-replay-engine/source/frontend folder to the mre-frontend folder using the below command.
```
 rsync -r --exclude 'mre-frontend' --exclude 'node_modules' --exclude 'cdk' . mre-frontend
```
Finally, navigate to aws-media-replay-engine/source/frontend/mre-frontend folder, commit and push the changes to AWS CodeCommit.
AWS Amplify CI/CD pipeline should now automatically build and deploy the updated MRE Frontend application.

Outputs

If you choose to interact with the MRE framework using the REST APIs directly, you will need the below information from the Outputs tab of the ControlPlane CloudFormation stack:

EndpointURL is the endpoint for accessing the APIs to create, read, update, delete (CRUD) Plugins, Models, Profiles, and schedule Events, Replays for processing.

Architecture Overview

Architecture Components

Control plane

The control plane is an API Gateway endpoint that includes APIs to create and manage different components of the video clipping and highlights generation process. These include:

Segmentation and Detection Pipeline - An AWS Step Functions state machine generated dynamically with one or more user-defined Plugins (Lambda functions) to identify the mark-in (start) and mark-out (end) timestamp of the segments (clips) as well as detect interesting actions happening within those segments. To help decide the outcome of its analysis, a Plugin can optionally depend on a user-defined AI/ML model hosted either in AWS using services such as Rekognition, SageMaker, etc. or outside AWS. The pipeline is also configured to automatically publish different segmentation event notifications to an EventBridge event bus monitored by MRE for event-based downstream processing.
Clip Generation - A pre-defined AWS Step Functions state machine that is invoked as a part of the Segmentation and Detection pipeline to generate MP4 preview clips (for the GUI) and HLS manifest using the mark-in and mark-out timestamps of the identified segments. Clip generation, like the Segmentation and Detection pipeline, sends clip related event notifications to the MRE EventBridge event bus.
Replay Generation - Another pre-defined AWS Step Functions state machine which automatically selects segments containing key events to create Replay (Highlight) in various resolutions in popular video formats such as HLS and MP4. Replays can be chosen to be created in Catchup mode or after an Event is fully streamed and is completely event driven via Amazon EventBridge rules.
Data Export - Exports Event and Replay data into popular formats such as EDL and JSON via clip generation events triggered through Amazon EventBridge. This data can be optionally enriched (via a custom process) and ingested into video editing systems to create engaging fan user experience by overlaying video and other key event data on a timeline.

Data plane

The data plane is an API Gateway endpoint that includes APIs using which the Plugins within the Segmentation and Detection pipeline can store and retrieve media assets as well as the processing metadata. There are also quite a few helper APIs available in the data plane that the Plugins can use for performing complex data queries and manipulations.

Code Layout

Path	Description
deployment/	shell scripts and Dockerfile
deployment/build-and-deploy.sh	shell script to build and deploy the solution using AWS CDK
deployment/lambda_layer_factory/Dockerfile	install dependencies and create a container image
deployment/lambda_layer_factory/docker-entrypoint.sh	shell script to build and package the Lambda layers as zip files within the container
deployment/lambda_layer_factory/build-lambda-layer.sh	shell script to run docker for building and packaging the Lambda layers
docs/	shell scripts and code to build and deploy the API docs from source
samples/	folder containing sample plugins, profiles, and ML model notebooks
source/	source code folder
source/frontend/	source code folder for the Frontend application
source/api/controlplane/	source code folder for the control plane
source/api/controlplane/*/infrastructure/	control plane CDK application
source/api/controlplane/*/runtime/	control plane Chalice application
source/api/dataplane/	source code folder for the data plane
source/api/dataplane/infrastructure/	data plane CDK application
source/api/dataplane/runtime/	data plane Chalice application
source/lib/	source code folder for the custom Lambda layers
source/lib/MediaReplayEnginePluginHelper/	source code for the MediaReplayEnginePluginHelper library
source/lib/MediaReplayEngineWorkflowHelper/	source code for the MediaReplayEngineWorkflowHelper library
source/gateway/infrastructure/	API Gateway CDK application
source/gateway/runtime/	API Gateway Chalice application
source/shared/infrastructure/	Shared resources CDK application
source/backend/caching/infrastructure/	Segment Caching CDK application
source/backend/caching/runtime/	Segment Caching related Lambda functions
source/backend/clipgeneration/infrastructure/	ClipGeneration CDK application
source/backend/clipgeneration/runtime/	ClipGeneration related Lambda functions
source/backend/data_export/infrastructure/	Data Export CDK application
source/backend/data_export/runtime/	Data Export related Lambda functions
source/backend/event-life-cycle/infrastructure/	Event life cycle CDK application
source/backend/event-life-cycle/runtime/	Event life cycle related Lambda functions
source/backend/replay/infrastructure/	Replay CDK application
source/backend/replay/runtime/	Replay related Lambda functions
source/backend/workflow_trigger/infrastructure/	Workflow Trigger CDK application
source/backend/workflow_trigger/runtime/	Workflow Trigger related Lambda functions

Demo

Check out Create automated intelligent highlights and replays in AWS M&E Demo Landing Page to watch a demo of an automated Football (Soccer) video clipping pipeline built using MRE.

Developers

To know more about how MRE works and for instructions on how to build a video clipping application with MRE, refer to the Developer Guide.

Security

MRE uses AWS_IAM to authorize REST API requests for both the Control and Data planes. The following screenshot shows how to test authentication to the MRE Control plane API using Postman. Be sure to specify the AccessKey and SecretKey for your own AWS environment.

Run the MRE Frontend application locally

Navigate to source/frontend folder.
Duplicate env.template and rename the duplicated file to .env.
Update the keys in .env with values from the Outputs tab of mre-frontend stack in AWS CloudFormation console.
Once the values are updated, run the below commands:

npm install

npm start

Cost

You are responsible for the cost of the AWS services used while running this solution.

Approximate cost (excluding free tiers):

AWS Service	Quantity	Cost
Amazon API Gateway	150000 requests	$0.16
Amazon DynamoDB	750000 writes, 146250 reads, 0.30 GB storage	$1.18
AWS Lambda	12000 invocations, 2-minute avg. duration, 256 MB memory	$6
AWS Step Functions	92400 state transitions	$2.21
Amazon S3	10 GB storage, 4000 PUT requests, 4000 GET requests	$0.26
AWS Elemental MediaConvert	240 minutes	$4.08
Amazon Rekognition	9000 Image analysis, 3 Custom Label inference units	$22.32
Amazon SageMaker	2 inference endpoints	$5.13

These cost estimates are for a video clipping and replay (highlight) generation pipeline built using MRE to segment a Tennis game with a duration of 3 hours. This specific pipeline had a total of 4 plugins included in the profile (with 2 of those plugins using Machine Learning models hosted in Rekognition and SageMaker). At the end of the game, the pipeline outputted a total of 282 Tennis clips.

NOTE: For tips on how to reduce the processing cost of a pipeline built using MRE, please refer to the Operations Developer Guide.

Limitations

While MRE deploys all the relevant AWS resources to facilitate automated video clipping and replay generation pipelines, you are still responsible for managing the service limits of those AWS resources via either AWS Service Quotas or AWS Support Center.
MRE currently uses a custom resource provider (based on Lambda) to work around a known AWS CDK limitation (aws/aws-cdk#12246) by creating the DynamoDB GSIs programmatically and polling them periodically to know when they become active. As of today, the custom resource provider in CDK has a maximum timeout limit of 2 hours and in some cases where the PluginResult table has more data (>20 GB), it could cause the Dataplane stack deployment to fail (especially when upgrading MRE to v2.5.1 or later). If this happens to you, please try one of the following workarounds:
- Delete old and unwanted Events via the MRE Frontend or DELETE /event/{name}/program/{program} Controlplane API.
- Backup existing PluginResult table, purge it, rerun the MRE installer, and then restore the data from backup.

Uninstall

Option 1: Uninstall using AWS CDK

# Delete the Frontend stack
cd aws-media-replay-engine/source/frontend/cdk
cdk destroy [--profile <aws-profile>]

# Delete the Gateway API stack
cd aws-media-replay-engine/source/gateway/cdk
cdk destroy [--profile <aws-profile>]

# Delete the Dataplane stack
cd aws-media-replay-engine/source/api/dataplane/infrastructure
cdk destroy [--profile <aws-profile>]

# Delete the Controlplane stacks 
cd aws-media-replay-engine/source/api/controlplane/replay/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/api/controlplane/event/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/api/controlplane/workflow/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/api/controlplane/profile/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/api/controlplane/plugin/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/api/controlplane/model/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/api/controlplane/system/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/api/controlplane/contentgroup/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/api/controlplane/program/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/api/custompriorities/program/infrastructure
cdk destroy [--profile <aws-profile>]

# Delete the remaining stacks

cd aws-media-replay-engine/source/backend/workflow_trigger/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/backend/replay/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/backend/event-life-cycle/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/backend/data_export/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/backend/clipgeneration/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/backend/caching/infrastructure
cdk destroy [--profile <aws-profile>]

cd aws-media-replay-engine/source/shared/infrastructure
cdk destroy [--profile <aws-profile>]

Option 2: Uninstall using the AWS Management Console

Sign-in to the AWS CloudFormation console.
Select the MRE Frontend stack.
Choose Delete.
Select the aws-mre-gateway stack.
Choose Delete.
Select the MRE Dataplane stack (aws-mre-dataplane).
Choose Delete.
Select all Controlplane stacks with the prefix "aws-mre-controlplane-" and delete them in the the order as outlined in the section Option 1 (Uninstall using AWS CDK).
Select and delete the following stacks
1. aws-mre-workflow-trigger
2. aws-mre-replay-handler
3. aws-mre-event-scheduler
4. aws-mre-event-life-cycle
5. aws-mre-data-exporter
6. aws-mre-clip-generation
7. aws-mre-segment-caching
8. aws-mre-shared-resources

Option 3: Uninstall using AWS Command Line Interface

aws cloudformation delete-stack --stack-name <frontend-stack-name> --region <aws-region>

aws cloudformation delete-stack --stack-name <gateway-stack-name> --region <aws-region>

aws cloudformation delete-stack --stack-name <dataplane-stack-name> --region <aws-region>

Repeat this command for all stacks with the prefix aws-mre-controlplane- Delete the controlplane stacks in the the order as outlined in the section Option 1 (Uninstall using AWS CDK).

aws cloudformation delete-stack --stack-name <aws-mre-controlplane-*> --region <aws-region>

Delete the remaining Stacks

aws cloudformation delete-stack --stack-name aws-mre-workflow-trigger --region <aws-region>
aws cloudformation delete-stack --stack-name aws-mre-replay-handler --region <aws-region>
aws cloudformation delete-stack --stack-name aws-mre-event-scheduler --region <aws-region>
aws cloudformation delete-stack --stack-name aws-mre-event-life-cycle --region <aws-region>
aws cloudformation delete-stack --stack-name aws-mre-data-exporter --region <aws-region>
aws cloudformation delete-stack --stack-name aws-mre-clip-generation --region <aws-region>
aws cloudformation delete-stack --stack-name aws-mre-segment-caching --region <aws-region>
aws cloudformation delete-stack --stack-name aws-mre-shared-resources --region <aws-region>

Deleting S3 buckets created by MRE

MRE creates 5 S3 buckets that are not automatically deleted. To delete these buckets, follow the steps below:

Sign in to the Amazon S3 console.
Select the LambdaLayerBucket bucket.
Choose Empty.
Choose Delete.
Select the MreMediaSourceBucket bucket.
Choose Empty.
Choose Delete.
Select the MreMediaOutputBucket bucket.
Choose Empty.
Choose Delete.
Select the MreSegmentCacheBucket bucket.
Choose Empty.
Choose Delete.
Select the MreDataExportBucket bucket.
Choose Empty.
Choose Delete.
Select the MreAccessLogsBucket bucket.
Choose Empty.
Choose Delete.

To delete the S3 bucket using AWS CLI, run the following command:

aws s3 rb s3://<bucket-name> --force

Bucket Policy (BYOB)

When utilizing a bucket where you are not the owner, only the bucket owner can configure notifications on a bucket. However, bucket owners can use a bucket policy to grant permission to other users to set this configuration with s3:PutBucketNotification permission.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "TriggerPermission",
            "Effect": "Allow",
            "Principal": {
                "Service": "lambda.amazonaws.com"
            },
            "Action": "s3:PutBucketNotification",
            "Resource": "arn:aws:s3:::{BUCKET_NAME}",
            "Condition": {
                "StringEquals": {
                    "aws:SourceAccount": "{ACCOUNT_NUMBER}"
                }
            }
        }
    ]
}

If you have a restrictive bucket policy that would explicitly prevent bucket access when providing your own bucket to MRE workflow- add the following policy to your bucket to allow access

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Sid": "MediaConvertRoleGet",
			"Principal": {
				"AWS": "arn:aws:iam::{ACCOUNT_NUMBER}:role/{MREMediaConvertIamRole from aws-mre-shared-resources stack}"
			},
			"Effect": "Allow",
			"Action": [
				"s3:GetObject",
				"s3:ListBucket"
			],
			"Resource": [
				"arn:aws:s3:::{BUCKET_NAME}",
				"arn:aws:s3:::{BUCKET_NAME}/*"
			]
		},
		{
			"Sid": "ProbeVideoRoleGet",
			"Principal": {
				"AWS": "arn:aws:iam::{ACCOUNT_NUMBER}:role/{ProbeVideoLambdaRole from aws-mre-controlplane-profile stack}"
			},
			"Effect": "Allow",
			"Action": [
				"s3:GetObject",
				"s3:ListBucket"
			],
			"Resource": [
				"arn:aws:s3:::{BUCKET_NAME}",
				"arn:aws:s3:::{BUCKET_NAME}/*"
			]
		},
		{
			"Sid": "AllowS3Trigger",
			"Principal": {
				"AWS": "arn:aws:iam::{ACCOUNT_NUMBER}:role/{ChaliceRole from aws-mre-controlplane-event stack}"
			},
			"Effect": "Allow",
			"Action": [
				"s3:*BucketNotification*",
			],
			"Resource": [
				"arn:aws:s3:::{BUCKET_NAME}"
			]
		},
        {
			"Sid": "AllowS3Selection",
			"Principal": {
				"AWS": "arn:aws:iam::{ACCOUNT_NUMBER}:role/{ChaliceRole from aws-mre-controlplane-system stack}"
			},
			"Effect": "Allow",
			"Action": [
				"s3:List*Bucket*"
			],
			"Resource": [
				"arn:aws:s3:::{BUCKET_NAME}"
			]
		}
	]
}

Contributing

See the CONTRIBUTING file for how to contribute.

License

The MRE Samples is licensed under MIT-0 license while the rest of the project is licensed under Apache-2.0 license.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

Name		Name	Last commit message	Last commit date
Latest commit History 79 Commits
.github		.github
deployment		deployment
docs		docs
samples		samples
source		source
tests		tests
.gitignore		.gitignore
CHANGELOG.md		CHANGELOG.md
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
MRE-Developer-Guide.md		MRE-Developer-Guide.md
MRE-Migration.md		MRE-Migration.md
NOTICE		NOTICE
README.md		README.md

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Repository files navigation

Install

Prerequisites

Greenfield Deployment

Upgrading

MRE Backend

MRE Frontend

Outputs

Architecture Overview

Architecture Components

Control plane

Data plane

Code Layout

Demo

Developers

Security

Run the MRE Frontend application locally

Cost

Approximate cost (excluding free tiers):

Limitations

Uninstall

Option 1: Uninstall using AWS CDK

Option 2: Uninstall using the AWS Management Console

Option 3: Uninstall using AWS Command Line Interface

Deleting S3 buckets created by MRE

Bucket Policy (BYOB)

Contributing

License

About

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License

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Install

Prerequisites

Greenfield Deployment

Upgrading

MRE Backend

MRE Frontend

Outputs

Architecture Overview

Architecture Components

Control plane

Data plane

Code Layout

Demo

Developers

Security

Run the MRE Frontend application locally

Cost

Approximate cost (excluding free tiers):

Limitations

Uninstall

Option 1: Uninstall using AWS CDK

Option 2: Uninstall using the AWS Management Console

Option 3: Uninstall using AWS Command Line Interface

Deleting S3 buckets created by MRE

Bucket Policy (BYOB)

Contributing

License

About

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