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 == 3.11
aws-cli
aws-cdk >= 2.24.1
docker
node >= 20.10.0
npm >= 10.2.3
git

NOTE: Using an Amazon EC2 Instance is the SUPPORTED and RECOMMENDED method to install MRE:

Use the env-init.sh script as User Data when launching your EC2 instance to automatically install all prerequisites

Configure permissions as appropriate for CDK usage (see env-role-permissions.json for policy statement example)

Tested configuration:

Amazon Linux 2023 (AL2023)

Architecture: x86_64

Instance Type: t2.large

EBS Storage: 16 GB gp3 (minimum requirement)

Before deployment:

Activate the Python virtual environment created by env-init.sh:

source mredeploy-env/bin/activate (located in /home/ec2-user/mredeploy/)

Ensure your environment variables are set as needed

IMPORTANT: You are responsible for stopping or terminating your EC2 instance after deployment to avoid unnecessary charges

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=2.9.1
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> --admin-email <admin-email>]

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=2.9.1
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 GitHub or GitLab. As a part of MRE Greenfield deployment, AWS Amplify temporarily uses Amazon S3 to store the branch assets while deploying.

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

NOTE: The warning message about redeploying the frontend application after running the above command can be ignored as we will be doing that shortly.

Run python3 create-env-file.py $REGION
Run npm i --legacy-peer-deps and npm run build from aws-media-replay-engine/source/frontend folder.
Zip the build assets located in aws-media-replay-engine/source/frontend/build folder zip -r -q -X ./build.zip *.
Run python3 init-amplify.py $REGION Update

Run Frontend Locally

If you have the MRE Framework deployed to AWS you can configure and run the MRE frontend without having to redeploy the frontend stack. This can be useful if you want to quickly experiment and tinker with the user interface.

Firstly you want to configure the frontend to use the correct AWS resources, you can create a .env file in the aws-media-replay-engine/source/frontend folder by copying the env.template file (executing python3 create-env-file.py $REGION will also create a .env file for you)
Fill in this env file with the correct information, this can all be found in the outputs of the MRE cloudformation stacks.
Once the .env file is filled you can now install and run the MRE frontend from your local machine with the below commands:

npm i --legacy-peer-deps
npm run start

This will install the packages needed by the frontend and then run it on localhost:3000. Be sure that you have Node 20.10.0 installed for this to work as intended.

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.

Improve HTTPS viewer connection security

The CloudFront distribution deployed by MRE allows for SSLv3 or TLSv1 for HTTPS viewer connections. However, it is recommended that customers help protect viewer connections by specifying a viewer certificate that enforces a minimum of TLSv1.2 in the security policy. In order to enforce TLSv1.2 in the security policy, update the Cloudfront Distribution by specifying a CNAME and the location of the SSL/TLS certificate. For more information on this, please refer to Cloudfront Distribution Viewer Certificate

❗ Important!

After a CNAME has been configured for the MRE Cloudfront distribution, be sure to execute the following steps to reflect this change within MRE deployment.

Navigate to AWS Systems Manager in the AWS Management console. Choose Parameter store and replace the value of the parameter /MRE/ControlPlane/MediaOutputDistribution with the CNAME of the MRE Cloudfront distribution.
Navigate to AWS Amplify in the AWS Management console. Under All apps, choose mre_frontend. In the left pane, under app settings, choose Environment Variables. Change the value of the environment variable REACT_APP_CLOUDFRONT_DOMAIN_NAME to the CNAME of the MRE Cloudfront distribution.
In the left pane, under app settings, choose Custom Headers. Edit the Header information by replacing the the default MRE Cloudfront distribution url with the CNAME of the MRE Cloudfront distribution.
Within the AWS Amplify console, under All apps, choose MRE Frontend. Click on Build and then click on Redeploy this version. This will redeploy the MRE frontend taking into account the new CNAME for the Cloudfront distribution.

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 script

Execute uninstall.sh

Option 3: 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 115 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

Run Frontend Locally

Outputs

Architecture Overview

Architecture Components

Control plane

Data plane

Code Layout

Demo

Developers

Security

Improve HTTPS viewer connection security

❗ Important!

Cost

Approximate cost (excluding free tiers):

Limitations

Uninstall

Option 1: Uninstall using AWS CDK

Option 2: Uninstall using script

Option 3: 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

Releases 14

Packages

Contributors 8

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License

awslabs/aws-media-replay-engine

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Install

Prerequisites

Greenfield Deployment

Upgrading

MRE Backend

MRE Frontend

Run Frontend Locally

Outputs

Architecture Overview

Architecture Components

Control plane

Data plane

Code Layout

Demo

Developers

Security

Improve HTTPS viewer connection security

❗ Important!

Cost

Approximate cost (excluding free tiers):

Limitations

Uninstall

Option 1: Uninstall using AWS CDK

Option 2: Uninstall using script

Option 3: 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

Resources

License

Code of conduct

Security policy

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Releases 14

Packages 0

Contributors 8

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