Expert-level media production, content management, streaming, broadcasting, and media technology systems
Expert guidance for media production, content management systems, video streaming, broadcasting systems, and modern media technology solutions.
from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import List, Optional, Dict
from decimal import Decimal
from enum import Enum
import hashlib
class MediaType(Enum):
VIDEO = "video"
AUDIO = "audio"
IMAGE = "image"
DOCUMENT = "document"
class AssetStatus(Enum):
DRAFT = "draft"
IN_REVIEW = "in_review"
APPROVED = "approved"
PUBLISHED = "published"
ARCHIVED = "archived"
@dataclass
class MediaAsset:
"""Media asset information"""
asset_id: str
title: str
description: str
media_type: MediaType
file_path: str
file_size_bytes: int
duration_seconds: Optional[float]
resolution: Optional[str] # e.g., "1920x1080"
codec: Optional[str]
bitrate_kbps: Optional[int]
frame_rate: Optional[float]
created_at: datetime
created_by: str
status: AssetStatus
tags: List[str]
metadata: Dict[str, str]
checksum: str
@dataclass
class ContentPackage:
"""Content package for distribution"""
package_id: str
title: str
assets: List[str] # Asset IDs
created_at: datetime
scheduled_publish: Optional[datetime]
expiration_date: Optional[datetime]
distribution_channels: List[str]
class MediaAssetManagementSystem:
"""Media asset management and workflow"""
def __init__(self):
self.assets = {}
self.packages = {}
self.workflows = []
def ingest_asset(self, file_path: str, metadata: dict) -> MediaAsset:
"""Ingest media asset into system"""
# Calculate checksum
checksum = self._calculate_checksum(file_path)
# Extract technical metadata
tech_metadata = self._extract_metadata(file_path)
asset = MediaAsset(
asset_id=self._generate_asset_id(),
title=metadata['title'],
description=metadata.get('description', ''),
media_type=MediaType(metadata['media_type']),
file_path=file_path,
file_size_bytes=tech_metadata['file_size'],
duration_seconds=tech_metadata.get('duration'),
resolution=tech_metadata.get('resolution'),
codec=tech_metadata.get('codec'),
bitrate_kbps=tech_metadata.get('bitrate'),
frame_rate=tech_metadata.get('frame_rate'),
created_at=datetime.now(),
created_by=metadata['created_by'],
status=AssetStatus.DRAFT,
tags=metadata.get('tags', []),
metadata=metadata.get('custom_metadata', {}),
checksum=checksum
)
self.assets[asset.asset_id] = asset
# Trigger automated workflows
self._trigger_workflows(asset)
return asset
def _extract_metadata(self, file_path: str) -> dict:
"""Extract technical metadata from media file"""
# Would use ffprobe or similar tool
# Simulated metadata
return {
'file_size': 1073741824, # 1 GB
'duration': 3600.0, # 1 hour
'resolution': '1920x1080',
'codec': 'h264',
'bitrate': 5000,
'frame_rate': 29.97
}
def _calculate_checksum(self, file_path: str) -> str:
"""Calculate file checksum for integrity"""
# In production, would read actual file
return hashlib.sha256(file_path.encode()).hexdigest()
def transcode_asset(self, asset_id: str, output_profiles: List[dict]) -> dict:
"""Transcode asset to multiple formats"""
asset = self.assets.get(asset_id)
if not asset:
return {'error': 'Asset not found'}
transcode_jobs = []
for profile in output_profiles:
job = {
'job_id': self._generate_job_id(),
'asset_id': asset_id,
'profile_name': profile['name'],
'target_resolution': profile['resolution'],
'target_bitrate': profile['bitrate'],
'target_codec': profile['codec'],
'status': 'queued',
'progress_percent': 0,
'estimated_completion': datetime.now() + timedelta(hours=1)
}
transcode_jobs.append(job)
return {
'asset_id': asset_id,
'transcode_jobs': transcode_jobs,
'total_jobs': len(transcode_jobs)
}
def search_assets(self, query: dict) -> List[MediaAsset]:
"""Search assets by metadata"""
results = []
for asset in self.assets.values():
match = True
# Text search
if 'keywords' in query:
keywords = query['keywords'].lower()
if keywords not in asset.title.lower() and keywords not in asset.description.lower():
match = False
# Media type filter
if 'media_type' in query and asset.media_type.value != query['media_type']:
match = False
# Status filter
if 'status' in query and asset.status.value != query['status']:
match = False
# Tag filter
if 'tags' in query:
required_tags = set(query['tags'])
asset_tags = set(asset.tags)
if not required_tags.issubset(asset_tags):
match = False
# Date range
if 'created_after' in query and asset.created_at < query['created_after']:
match = False
if match:
results.append(asset)
return results
def create_content_package(self, package_data: dict) -> ContentPackage:
"""Create content package for distribution"""
package = ContentPackage(
package_id=self._generate_package_id(),
title=package_data['title'],
assets=package_data['asset_ids'],
created_at=datetime.now(),
scheduled_publish=package_data.get('scheduled_publish'),
expiration_date=package_data.get('expiration_date'),
distribution_channels=package_data['channels']
)
self.packages[package.package_id] = package
return package
def analyze_content(self, asset_id: str) -> dict:
"""AI-powered content analysis"""
asset = self.assets.get(asset_id)
if not asset:
return {'error': 'Asset not found'}
# Simulate AI analysis
analysis = {
'asset_id': asset_id,
'detected_objects': ['person', 'car', 'building'],
'detected_scenes': ['outdoor', 'daytime', 'urban'],
'faces_detected': 3,
'speech_to_text': 'Transcribed content would appear here...',
'sentiment': 'positive',
'content_categories': ['news', 'documentary'],
'suggested_tags': ['urban', 'interview', 'documentary'],
'quality_score': 85.0
}
return analysis
def _trigger_workflows(self, asset: MediaAsset):
"""Trigger automated workflows for asset"""
# Trigger proxy generation, thumbnails, etc.
pass
def _generate_asset_id(self) -> str:
import uuid
return f"ASSET-{uuid.uuid4().hex[:12].upper()}"
def _generate_package_id(self) -> str:
import uuid
return f"PKG-{uuid.uuid4().hex[:8].upper()}"
def _generate_job_id(self) -> str:
import uuid
return f"JOB-{uuid.uuid4().hex[:8].upper()}"
class VideoStreamingPlatform:
"""Video streaming and delivery system"""
def __init__(self):
self.streams = {}
self.viewers = {}
self.cdn_nodes = {}
def start_live_stream(self, stream_data: dict) -> dict:
"""Start live video stream"""
stream_id = self._generate_stream_id()
stream = {
'stream_id': stream_id,
'title': stream_data['title'],
'description': stream_data.get('description', ''),
'streamer_id': stream_data['streamer_id'],
'status': 'live',
'started_at': datetime.now(),
'viewer_count': 0,
'peak_viewers': 0,
'ingest_url': f'rtmp://ingest.example.com/live/{stream_id}',
'playback_urls': {
'hls': f'https://cdn.example.com/live/{stream_id}/playlist.m3u8',
'dash': f'https://cdn.example.com/live/{stream_id}/manifest.mpd'
},
'quality_profiles': ['1080p', '720p', '480p', '360p']
}
self.streams[stream_id] = stream
return stream
def generate_adaptive_bitrate_manifest(self, asset_id: str) -> dict:
"""Generate ABR manifest for adaptive streaming"""
# Generate HLS manifest
hls_variants = [
{
'bandwidth': 5000000, # 5 Mbps
'resolution': '1920x1080',
'codecs': 'avc1.640028,mp4a.40.2',
'url': f'1080p/playlist.m3u8'
},
{
'bandwidth': 2800000, # 2.8 Mbps
'resolution': '1280x720',
'codecs': 'avc1.64001f,mp4a.40.2',
'url': f'720p/playlist.m3u8'
},
{
'bandwidth': 1400000, # 1.4 Mbps
'resolution': '854x480',
'codecs': 'avc1.64001e,mp4a.40.2',
'url': f'480p/playlist.m3u8'
},
{
'bandwidth': 800000, # 800 Kbps
'resolution': '640x360',
'codecs': 'avc1.64001e,mp4a.40.2',
'url': f'360p/playlist.m3u8'
}
]
return {
'asset_id': asset_id,
'protocol': 'hls',
'master_playlist_url': f'https://cdn.example.com/vod/{asset_id}/master.m3u8',
'variants': hls_variants
}
def track_viewer_metrics(self, stream_id: str, viewer_id: str) -> dict:
"""Track viewer engagement metrics"""
metrics = {
'stream_id': stream_id,
'viewer_id': viewer_id,
'watch_time_seconds': 3600,
'buffer_events': 2,
'average_bitrate': 3500000,
'quality_switches': 5,
'playback_start_time_ms': 1200,
'errors': 0,
'device_type': 'desktop',
'browser': 'chrome'
}
# Calculate Quality of Experience (QoE)
qoe_score = self._calculate_qoe(metrics)
metrics['qoe_score'] = qoe_score
return metrics
def _calculate_qoe(self, metrics: dict) -> float:
"""Calculate Quality of Experience score"""
score = 100.0
# Penalize buffering
score -= metrics['buffer_events'] * 5
# Penalize startup time
if metrics['playback_start_time_ms'] > 2000:
score -= 10
# Penalize errors
score -= metrics['errors'] * 15
return max(0.0, score)
def implement_drm(self, asset_id: str, drm_config: dict) -> dict:
"""Implement Digital Rights Management"""
drm = {
'asset_id': asset_id,
'drm_systems': {
'widevine': {
'license_url': 'https://license.example.com/widevine',
'supported_levels': ['L1', 'L3']
},
'fairplay': {
'certificate_url': 'https://license.example.com/fairplay/cert',
'license_url': 'https://license.example.com/fairplay/license'
},
'playready': {
'license_url': 'https://license.example.com/playready'
}
},
'encryption': 'AES-128-CTR',
'key_rotation_interval': 3600 # seconds
}
return drm
def optimize_cdn_delivery(self, asset_id: str, viewer_location: tuple) -> dict:
"""Optimize CDN delivery based on viewer location"""
# Find nearest CDN edge node
nearest_node = self._find_nearest_cdn_node(viewer_location)
return {
'asset_id': asset_id,
'cdn_node': nearest_node['node_id'],
'cdn_location': nearest_node['location'],
'distance_km': nearest_node['distance'],
'estimated_latency_ms': nearest_node['latency'],
'delivery_url': f"https://{nearest_node['node_id']}.cdn.example.com/{asset_id}"
}
def _find_nearest_cdn_node(self, viewer_location: tuple) -> dict:
"""Find nearest CDN edge node to viewer"""
# Would calculate actual distances to CDN nodes
return {
'node_id': 'edge-us-east-1',
'location': 'Virginia, USA',
'distance': 250, # km
'latency': 15 # ms
}
def _generate_stream_id(self) -> str:
import uuid
return f"STREAM-{uuid.uuid4().hex[:8].upper()}"
class BroadcastAutomationSystem:
"""Broadcast scheduling and automation"""
def __init__(self):
self.schedule = []
self.playlists = {}
def create_broadcast_schedule(self, channel: str, date: datetime, programming: List[dict]) -> dict:
"""Create daily broadcast schedule"""
schedule_items = []
current_time = date.replace(hour=0, minute=0, second=0)
for program in programming:
item = {
'channel': channel,
'start_time': current_time,
'end_time': current_time + timedelta(seconds=program['duration']),
'program_title': program['title'],
'asset_id': program['asset_id'],
'type': program['type'], # 'program', 'commercial', 'filler'
'metadata': program.get('metadata', {})
}
schedule_items.append(item)
current_time = item['end_time']
self.schedule.extend(schedule_items)
return {
'channel': channel,
'date': date.date().isoformat(),
'total_items': len(schedule_items),
'total_duration': (schedule_items[-1]['end_time'] - schedule_items[0]['start_time']).seconds,
'schedule': schedule_items[:5] # Return first 5 items
}
def generate_playlist(self, schedule_id: str) -> dict:
"""Generate playout playlist"""
# Convert schedule to playout format
playlist = {
'playlist_id': self._generate_playlist_id(),
'format': 'xml', # or 'json'
'items': []
}
return playlist
def monitor_broadcast(self, channel: str) -> dict:
"""Monitor live broadcast status"""
status = {
'channel': channel,
'on_air': True,
'current_program': 'Evening News',
'time_code': '00:15:32',
'next_program': 'Sports Tonight',
'next_program_in': 2728, # seconds
'signal_quality': {
'video_ok': True,
'audio_ok': True,
'sync_ok': True
},
'alarms': []
}
return status
def _generate_playlist_id(self) -> str:
import uuid
return f"PLAY-{uuid.uuid4().hex[:8].upper()}"
❌ No backup systems ❌ Poor metadata management ❌ Single bitrate streaming ❌ No CDN implementation ❌ Ignoring QoE metrics ❌ Manual scheduling processes ❌ No DRM for premium content ❌ Poor asset organization ❌ No disaster recovery plan