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.

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

NOTE: If using an AWS Cloud9 environment to install MRE:

• Choose Amazon Linux 2 as the platform for your Cloud9 environment.
• Resize the EBS volume to at least 15 GB by following this guide.
• Ensure the installed version of Python is 3.11. There is a script that will do this for you on Cloud9 Amazon Linux 2 environments.

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=2.9.0
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>]

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=2.9.0
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 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.

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.
• 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.

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.

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.

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.

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.

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.

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.

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

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.

1. 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.
2. 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.
3. 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.
4. 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.

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

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.

• 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.

# 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>]

1. Sign-in to the AWS CloudFormation console.
2. Select the MRE Frontend stack.
3. Choose Delete.
4. Select the aws-mre-gateway stack.
5. Choose Delete.
6. Select the MRE Dataplane stack (aws-mre-dataplane).
7. Choose Delete.
8. 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).
9. 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

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>

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

1. Sign in to the Amazon S3 console.
2. Select the LambdaLayerBucket bucket.
3. Choose Empty.
4. Choose Delete.
5. Select the MreMediaSourceBucket bucket.
6. Choose Empty.
7. Choose Delete.
8. Select the MreMediaOutputBucket bucket.
9. Choose Empty.
10. Choose Delete.
11. Select the MreSegmentCacheBucket bucket.
12. Choose Empty.
13. Choose Delete.
14. Select the MreDataExportBucket bucket.
15. Choose Empty.
16. Choose Delete.
17. Select the MreAccessLogsBucket bucket.
18. Choose Empty.
19. Choose Delete.

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

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

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}"
			]
		}
	]
}

See the CONTRIBUTING file for how to contribute.

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

Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.

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.