Proxy Infrastructure for Video Streaming and CDN Optimization

Video streaming has become the dominant form of internet traffic, accounting for over 80% of global data consumption. As audiences demand higher quality content with minimal buffering and global accessibility, proxy infrastructure has emerged as a critical component in optimizing video delivery networks. This comprehensive guide explores how proxy networks enhance streaming performance, reduce latency, and enable seamless content distribution across global audiences.

The Challenge of Global Video Delivery

Bandwidth and Latency Constraints

Global Reach Requirements: Modern streaming platforms must deliver content to users worldwide, each with different network conditions, device capabilities, and geographic constraints. Quality Expectations: Users expect 4K, 8K, and HDR content with instant startup times and zero buffering, regardless of their location or network infrastructure. Scale Challenges: Popular streaming events can generate millions of concurrent viewers, requiring infrastructure that can scale dynamically to meet demand spikes.

Network Optimization Needs

Last-Mile Performance: The final network hop to users often represents the biggest bottleneck in content delivery, requiring intelligent routing and caching strategies. Regional Content Laws: Different regions have varying content licensing requirements, necessitating geo-aware content delivery systems. Device Diversity: Content must be optimized for everything from mobile devices to smart TVs, each with different codec support and bandwidth capabilities.

Proxy Architecture for Streaming Optimization

Multi-Tier Caching Strategy

Origin Shield Proxies:

from typing import Dict, List, Optional, Tuple



class OriginShieldProxy:
    def __init__(self, shield_config: Dict[str, str]):
        self.shield_config = shield_config
        self.cache_storage = {}
        self.cache_metadata = {}
        self.origin_connections = {}
        self.bandwidth_monitor = BandwidthMonitor()
        
    async def handle_streaming_request(self, request_info: Dict[str, str]) -> Tuple[bytes, Dict[str, str]]:
        """Handle streaming content request with intelligent caching"""
        content_id = request_info['content_id']
        quality = request_info.get('quality', '1080p')
        segment_number = request_info.get('segment', 0)
        
        # Generate cache key
        cache_key = self._generate_cache_key(content_id, quality, segment_number)
        
        # Check cache first
        cached_content = await self._get_from_cache(cache_key)
        if cached_content:
            await self._update_cache_stats(cache_key, 'hit')
            return cached_content, {'cache_status': 'hit', 'source': 'shield'}
        
        # Fetch from origin with connection pooling
        origin_content = await self._fetch_from_origin(request_info)
        
        # Cache the content with appropriate TTL
        await self._store_in_cache(cache_key, origin_content, request_info)
        await self._update_cache_stats(cache_key, 'miss')
        
        return origin_content, {'cache_status': 'miss', 'source': 'origin'}
    
    def _generate_cache_key(self, content_id: str, quality: str, segment: int) -> str:
        """Generate unique cache key for content segment"""
        key_data = f"{content_id}:{quality}:{segment}"
        return hashlib.md5(key_data.encode()).hexdigest()
    
    async def _get_from_cache(self, cache_key: str) -> Optional[bytes]:
        """Retrieve content from cache if available and valid"""
        if cache_key not in self.cache_storage:
            return None
        
        metadata = self.cache_metadata.get(cache_key, {})
        
        # Check TTL
        if time.time() > metadata.get('expires_at', 0):
            await self._evict_from_cache(cache_key)
            return None
        
        # Update access time for LRU
        metadata['last_accessed'] = time.time()
        metadata['access_count'] += 1
        
        return self.cache_storage[cache_key]
    
    async def _fetch_from_origin(self, request_info: Dict[str, str]) -> bytes:
        """Fetch content from origin server with connection pooling"""
        origin_url = self._construct_origin_url(request_info)
        
        # Use persistent connection pool
        if 'origin_session' not in self.origin_connections:
            connector = aiohttp.TCPConnector(
                limit=100,
                limit_per_host=20,
                keepalive_timeout=30
            )
            self.origin_connections['origin_session'] = aiohttp.ClientSession(
                connector=connector,
                timeout=aiohttp.ClientTimeout(total=30)
            )
        
        session = self.origin_connections['origin_session']
        
        async with session.get(origin_url) as response:
            if response.status == 200:
                content = await response.read()
                
                # Monitor bandwidth usage
                await self.bandwidth_monitor.record_transfer(len(content))
                
                return content
            else:
                raise Exception(f"Origin fetch failed: {response.status}")
    
    async def _store_in_cache(self, cache_key: str, content: bytes, request_info: Dict[str, str]):
        """Store content in cache with metadata"""
        # Calculate TTL based on content type
        ttl = self._calculate_ttl(request_info)
        
        # Check if we need to evict old content
        await self._ensure_cache_space(len(content))
        
        # Store content and metadata
        self.cache_storage[cache_key] = content
        self.cache_metadata[cache_key] = {
            'created_at': time.time(),
            'expires_at': time.time() + ttl,
            'last_accessed': time.time(),
            'access_count': 1,
            'content_size': len(content),
            'content_type': request_info.get('content_type', 'video/mp4'),
            'quality': request_info.get('quality', '1080p')
        }
    
    def _calculate_ttl(self, request_info: Dict[str, str]) -> int:
        """Calculate cache TTL based on content characteristics"""
        content_type = request_info.get('content_type', 'video')
        
        # Live content gets shorter TTL
        if request_info.get('is_live', False):
            return 30  # 30 seconds for live content
        
        # Popular content gets longer TTL
        popularity_score = request_info.get('popularity_score', 0)
        if popularity_score > 0.8:
            return 86400  # 24 hours for popular content
        elif popularity_score > 0.5:
            return 43200  # 12 hours for moderately popular
        else:
            return 3600   # 1 hour for less popular content

class EdgeCacheProxy:
    def __init__(self, region: str, edge_config: Dict[str, str]):
        self.region = region
        self.edge_config = edge_config
        self.local_cache = {}
        self.cache_stats = {
            'hits': 0,
            'misses': 0,
            'evictions': 0,
            'bytes_served': 0
        }
        self.adaptive_bitrate_engine = AdaptiveBitrateEngine()
        
    async def serve_streaming_content(self, request: Dict[str, str]) -> Tuple[bytes, Dict[str, str]]:
        """Serve streaming content with adaptive bitrate optimization"""
        # Analyze client connection quality
        client_info = await self._analyze_client_connection(request)
        
        # Determine optimal quality level
        optimal_quality = await self.adaptive_bitrate_engine.select_quality(
            client_info, request.get('requested_quality', 'auto')
        )
        
        # Update request with optimal quality
        optimized_request = request.copy()
        optimized_request['quality'] = optimal_quality
        
        # Check local edge cache
        cache_key = self._generate_edge_cache_key(optimized_request)
        cached_content = self.local_cache.get(cache_key)
        
        if cached_content and self._is_cache_valid(cached_content):
            self.cache_stats['hits'] += 1
            self.cache_stats['bytes_served'] += len(cached_content['data'])
            
            return cached_content['data'], {
                'cache_status': 'edge_hit',
                'quality_served': optimal_quality,
                'region': self.region
            }
        
        # Fetch from shield proxy
        shield_content, shield_metadata = await self._fetch_from_shield(optimized_request)
        
        # Cache locally
        await self._cache_locally(cache_key, shield_content, optimized_request)
        
        self.cache_stats['misses'] += 1
        self.cache_stats['bytes_served'] += len(shield_content)
        
        return shield_content, {
            'cache_status': 'edge_miss',
            'quality_served': optimal_quality,
            'region': self.region,
            'shield_status': shield_metadata.get('cache_status', 'unknown')
        }
    
    async def _analyze_client_connection(self, request: Dict[str, str]) -> Dict[str, float]:
        """Analyze client connection characteristics"""
        client_ip = request.get('client_ip', '')
        user_agent = request.get('user_agent', '')
        
        # Estimate bandwidth based on client characteristics
        estimated_bandwidth = await self._estimate_client_bandwidth(client_ip)
        
        # Detect device capabilities
        device_info = self._parse_device_capabilities(user_agent)
        
        return {
            'estimated_bandwidth_mbps': estimated_bandwidth,
            'device_type': device_info['type'],
            'max_resolution': device_info['max_resolution'],
            'codec_support': device_info['codecs'],
            'connection_type': self._detect_connection_type(client_ip)
        }
    
    async def _estimate_client_bandwidth(self, client_ip: str) -> float:
        """Estimate client bandwidth based on IP geolocation and historical data"""
        # This would integrate with real bandwidth measurement services
        # For demo purposes, return estimated values based on IP location
        
        ip_info = await self._get_ip_geolocation(client_ip)
        country = ip_info.get('country', 'unknown')
        
        # Average bandwidth by country (simplified)
        bandwidth_estimates = {
            'US': 25.0,  # Mbps
            'GB': 22.0,
            'DE': 20.0,
            'JP': 18.0,
            'KR': 28.0,
            'SG': 24.0,
            'AU': 15.0,
            'BR': 12.0,
            'IN': 8.0,
            'unknown': 10.0
        }
        
        return bandwidth_estimates.get(country, 10.0)

class AdaptiveBitrateEngine:
    def __init__(self):
        self.quality_profiles = {
            '240p': {'bitrate': 0.4, 'resolution': (426, 240)},
            '360p': {'bitrate': 0.8, 'resolution': (640, 360)},
            '480p': {'bitrate': 1.5, 'resolution': (854, 480)},
            '720p': {'bitrate': 3.0, 'resolution': (1280, 720)},
            '1080p': {'bitrate': 6.0, 'resolution': (1920, 1080)},
            '1440p': {'bitrate': 12.0, 'resolution': (2560, 1440)},
            '4K': {'bitrate': 25.0, 'resolution': (3840, 2160)}
        }
        
    async def select_quality(self, client_info: Dict[str, float], 
                           requested_quality: str) -> str:
        """Select optimal quality based on client capabilities and network conditions"""
        bandwidth_mbps = client_info['estimated_bandwidth_mbps']
        device_max_res = client_info.get('max_resolution', (1920, 1080))
        
        # If specific quality requested and feasible, use it
        if requested_quality != 'auto' and requested_quality in self.quality_profiles:
            profile = self.quality_profiles[requested_quality]
            if (bandwidth_mbps >= profile['bitrate'] * 1.2 and  # 20% headroom
                self._resolution_supported(profile['resolution'], device_max_res)):
                return requested_quality
        
        # Find optimal quality based on bandwidth and device capabilities
        suitable_qualities = []
        
        for quality, profile in self.quality_profiles.items():
            if (bandwidth_mbps >= profile['bitrate'] * 1.5 and  # 50% headroom for stability
                self._resolution_supported(profile['resolution'], device_max_res)):
                suitable_qualities.append((quality, profile['bitrate']))
        
        if not suitable_qualities:
            return '240p'  # Fallback to lowest quality
        
        # Select highest quality that meets criteria
        suitable_qualities.sort(key=lambda x: x[1], reverse=True)
        return suitable_qualities[0][0]
    
    def _resolution_supported(self, content_res: Tuple[int, int], 
                            device_max_res: Tuple[int, int]) -> bool:
        """Check if device supports the content resolution"""
        return (content_res[0] <= device_max_res[0] and 
                content_res[1] <= device_max_res[1])

Geographic Load Balancing

Regional Traffic Distribution:

from typing import Dict, List, Optional



class GeographicLoadBalancer:
    def __init__(self):
        self.regional_servers = {}
        self.server_health = {}
        self.routing_policies = {}
        self.performance_metrics = {}
        
    def register_regional_server(self, region: str, 
                               server_config: Dict[str, str]) -> bool:
        """Register streaming server for specific region"""
        try:
            self.regional_servers[region] = {
                'endpoint': server_config['endpoint'],
                'capacity': server_config.get('capacity', 1000),  # concurrent streams
                'current_load': 0,
                'avg_latency': server_config.get('avg_latency', 50),  # ms
                'bandwidth_limit': server_config.get('bandwidth_gbps', 10),
                'current_bandwidth': 0,
                'content_cache': set(server_config.get('cached_content', []))
            }
            
            # Initialize health monitoring
            self.server_health[region] = {
                'status': 'healthy',
                'last_check': time.time(),
                'uptime_percentage': 100.0,
                'response_time': server_config.get('avg_latency', 50)
            }
            
            return True
            
        except Exception as e:
            print(f"Failed to register server for region {region}: {e}")
            return False
    
    async def route_streaming_request(self, request: Dict[str, str]) -> Optional[str]:
        """Route streaming request to optimal regional server"""
        client_ip = request.get('client_ip', '')
        content_id = request.get('content_id', '')
        quality = request.get('quality', '1080p')
        
        # Determine client location
        client_region = await self._get_client_region(client_ip)
        
        # Get candidate servers
        candidates = await self._get_candidate_servers(
            client_region, content_id, quality
        )
        
        if not candidates:
            return None
        
        # Select optimal server based on multiple factors
        optimal_server = await self._select_optimal_server(
            candidates, request, client_region
        )
        
        # Update server load tracking
        await self._update_server_load(optimal_server, request)
        
        return self.regional_servers[optimal_server]['endpoint']
    
    async def _get_candidate_servers(self, client_region: str, 
                                   content_id: str, 
                                   quality: str) -> List[str]:
        """Get list of candidate servers for request"""
        candidates = []
        
        for region, server in self.regional_servers.items():
            # Check server health
            if self.server_health[region]['status'] != 'healthy':
                continue
            
            # Check capacity
            if server['current_load'] >= server['capacity'] * 0.9:  # 90% capacity limit
                continue
            
            # Check bandwidth availability
            quality_bandwidth = self._get_quality_bandwidth(quality)
            if server['current_bandwidth'] + quality_bandwidth > server['bandwidth_limit']:
                continue
            
            # Prefer servers with content already cached
            cache_bonus = 1 if content_id in server['content_cache'] else 0
            
            candidates.append({
                'region': region,
                'cache_bonus': cache_bonus,
                'load_factor': server['current_load'] / server['capacity'],
                'latency': self._estimate_latency(client_region, region)
            })
        
        return candidates
    
    async def _select_optimal_server(self, candidates: List[Dict], 
                                   request: Dict[str, str], 
                                   client_region: str) -> str:
        """Select optimal server using weighted scoring"""
        if not candidates:
            return None
        
        best_server = None
        best_score = -1
        
        for candidate in candidates:
            # Calculate composite score
            score = self._calculate_server_score(candidate, client_region)
            
            if score > best_score:
                best_score = score
                best_server = candidate['region']
        
        return best_server
    
    def _calculate_server_score(self, candidate: Dict, client_region: str) -> float:
        """Calculate weighted score for server selection"""
        # Weight factors
        LATENCY_WEIGHT = 0.4
        LOAD_WEIGHT = 0.3
        CACHE_WEIGHT = 0.2
        PROXIMITY_WEIGHT = 0.1
        
        # Normalize latency (lower is better)
        latency_score = max(0, 1 - (candidate['latency'] / 200))  # Normalize to 200ms max
        
        # Normalize load (lower is better)
        load_score = 1 - candidate['load_factor']
        
        # Cache bonus (higher is better)
        cache_score = candidate['cache_bonus']
        
        # Geographic proximity bonus
        proximity_score = 1 if candidate['region'] == client_region else 0.5
        
        # Calculate weighted score
        total_score = (
            latency_score * LATENCY_WEIGHT +
            load_score * LOAD_WEIGHT +
            cache_score * CACHE_WEIGHT +
            proximity_score * PROXIMITY_WEIGHT
        )
        
        return total_score
    
    def _estimate_latency(self, client_region: str, server_region: str) -> float:
        """Estimate network latency between regions"""
        if client_region == server_region:
            return 10  # Local region
        
        # Simplified latency matrix (in practice, use real measurements)
        latency_matrix = {
            ('US', 'EU'): 80,
            ('US', 'ASIA'): 150,
            ('EU', 'ASIA'): 120,
            ('US', 'AU'): 180,
            ('EU', 'AU'): 200,
            ('ASIA', 'AU'): 100
        }
        
        # Check both directions
        key1 = (client_region, server_region)
        key2 = (server_region, client_region)
        
        return latency_matrix.get(key1, latency_matrix.get(key2, 100))

class StreamingAnalytics:
    def __init__(self):
        self.viewer_metrics = {}
        self.content_metrics = {}
        self.performance_data = {}
        
    async def track_viewer_session(self, session_id: str, 
                                 session_data: Dict[str, any]):
        """Track individual viewer session metrics"""
        self.viewer_metrics[session_id] = {
            'start_time': time.time(),
            'content_id': session_data['content_id'],
            'quality_changes': [],
            'buffer_events': [],
            'client_info': session_data.get('client_info', {}),
            'region': session_data.get('region', 'unknown'),
            'total_watch_time': 0,
            'bytes_transferred': 0
        }
    
    async def record_quality_change(self, session_id: str, 
                                  old_quality: str, 
                                  new_quality: str, 
                                  reason: str):
        """Record adaptive bitrate quality changes"""
        if session_id in self.viewer_metrics:
            self.viewer_metrics[session_id]['quality_changes'].append({
                'timestamp': time.time(),
                'from_quality': old_quality,
                'to_quality': new_quality,
                'reason': reason
            })
    
    async def record_buffer_event(self, session_id: str, 
                                event_type: str, 
                                duration_ms: float):
        """Record buffering events"""
        if session_id in self.viewer_metrics:
            self.viewer_metrics[session_id]['buffer_events'].append({
                'timestamp': time.time(),
                'event_type': event_type,  # 'start', 'end'
                'duration_ms': duration_ms
            })
    
    async def generate_performance_report(self, time_period: str = '1h') -> Dict[str, any]:
        """Generate comprehensive performance analytics report"""
        cutoff_time = time.time() - self._parse_time_period(time_period)
        
        # Filter recent sessions
        recent_sessions = {
            sid: data for sid, data in self.viewer_metrics.items()
            if data['start_time'] > cutoff_time
        }
        
        if not recent_sessions:
            return {'error': 'No data available for specified time period'}
        
        # Calculate key metrics
        total_sessions = len(recent_sessions)
        total_watch_time = sum(s['total_watch_time'] for s in recent_sessions.values())
        total_bytes = sum(s['bytes_transferred'] for s in recent_sessions.values())
        
        # Buffer ratio calculation
        total_buffer_time = 0
        buffer_event_count = 0
        
        for session in recent_sessions.values():
            for event in session['buffer_events']:
                if event['event_type'] == 'end':
                    total_buffer_time += event['duration_ms']
                    buffer_event_count += 1
        
        buffer_ratio = (total_buffer_time / 1000) / total_watch_time if total_watch_time > 0 else 0
        
        # Quality distribution
        quality_distribution = {}
        for session in recent_sessions.values():
            for change in session['quality_changes']:
                quality = change['to_quality']
                quality_distribution[quality] = quality_distribution.get(quality, 0) + 1
        
        # Regional performance
        regional_performance = {}
        for session in recent_sessions.values():
            region = session['region']
            if region not in regional_performance:
                regional_performance[region] = {
                    'session_count': 0,
                    'total_buffer_events': 0,
                    'avg_quality_changes': 0
                }
            
            regional_performance[region]['session_count'] += 1
            regional_performance[region]['total_buffer_events'] += len(session['buffer_events'])
            regional_performance[region]['avg_quality_changes'] += len(session['quality_changes'])
        
        # Calculate averages
        for region_data in regional_performance.values():
            if region_data['session_count'] > 0:
                region_data['avg_quality_changes'] /= region_data['session_count']
                region_data['avg_buffer_events'] = region_data['total_buffer_events'] / region_data['session_count']
        
        return {
            'time_period': time_period,
            'total_sessions': total_sessions,
            'total_watch_time_hours': total_watch_time / 3600,
            'total_bandwidth_gb': total_bytes / (1024**3),
            'average_session_duration_minutes': (total_watch_time / total_sessions) / 60 if total_sessions > 0 else 0,
            'buffer_ratio_percentage': buffer_ratio * 100,
            'total_buffer_events': buffer_event_count,
            'quality_distribution': quality_distribution,
            'regional_performance': regional_performance,
            'key_metrics': {
                'startup_time_ms': self._calculate_average_startup_time(recent_sessions),
                'rebuffer_rate': buffer_event_count / total_sessions if total_sessions > 0 else 0,
                'quality_consistency': self._calculate_quality_consistency(recent_sessions)
            }
        }

Advanced Streaming Features

Live Streaming Optimization

Real-Time Content Delivery:

from typing import Dict, List, Optional


class LiveStreamingOptimizer:
    def __init__(self):
        self.live_streams = {}
        self.edge_ingestors = {}
        self.transcoding_pipeline = {}
        self.real_time_metrics = {}
        
    async def start_live_stream(self, stream_id: str, 
                              stream_config: Dict[str, any]) -> bool:
        """Initialize live stream with real-time optimization"""
        try:
            # Set up transcoding pipeline
            transcoding_config = await self._setup_transcoding_pipeline(
                stream_id, stream_config
            )
            
            # Configure edge ingestors
            ingestor_config = await self._setup_edge_ingestors(
                stream_id, stream_config
            )
            
            # Initialize real-time metrics tracking
            self.real_time_metrics[stream_id] = {
                'start_time': time.time(),
                'viewers': 0,
                'peak_viewers': 0,
                'current_bitrate': 0,
                'transcoding_latency': 0,
                'edge_latency': {},
                'quality_levels': stream_config.get('quality_levels', ['720p', '1080p'])
            }
            
            # Store stream configuration
            self.live_streams[stream_id] = {
                'config': stream_config,
                'transcoding': transcoding_config,
                'ingestors': ingestor_config,
                'status': 'active',
                'start_time': time.time()
            }
            
            return True
            
        except Exception as e:
            print(f"Failed to start live stream {stream_id}: {e}")
            return False
    
    async def optimize_live_delivery(self, stream_id: str) -> Dict[str, any]:
        """Continuously optimize live stream delivery"""
        if stream_id not in self.live_streams:
            return {'error': 'Stream not found'}
        
        stream_data = self.live_streams[stream_id]
        metrics = self.real_time_metrics[stream_id]
        
        # Analyze current performance
        performance_analysis = await self._analyze_stream_performance(stream_id)
        
        # Adjust transcoding parameters
        transcoding_adjustments = await self._optimize_transcoding(
            stream_id, performance_analysis
        )
        
        # Optimize edge distribution
        edge_optimizations = await self._optimize_edge_distribution(
            stream_id, performance_analysis
        )
        
        # Implement adaptive streaming adjustments
        adaptive_adjustments = await self._adjust_adaptive_streaming(
            stream_id, performance_analysis
        )
        
        optimization_results = {
            'stream_id': stream_id,
            'timestamp': time.time(),
            'performance_analysis': performance_analysis,
            'optimizations_applied': {
                'transcoding': transcoding_adjustments,
                'edge_distribution': edge_optimizations,
                'adaptive_streaming': adaptive_adjustments
            },
            'expected_improvements': self._calculate_expected_improvements(
                transcoding_adjustments, edge_optimizations, adaptive_adjustments
            )
        }
        
        return optimization_results
    
    async def _analyze_stream_performance(self, stream_id: str) -> Dict[str, any]:
        """Analyze current stream performance metrics"""
        metrics = self.real_time_metrics[stream_id]
        
        # Calculate key performance indicators
        current_time = time.time()
        stream_duration = current_time - metrics['start_time']
        
        # Analyze viewer patterns
        viewer_growth_rate = 0
        if stream_duration > 300:  # 5 minutes minimum
            recent_viewers = metrics['viewers']
            viewer_growth_rate = recent_viewers / (stream_duration / 60)  # viewers per minute
        
        # Analyze latency patterns
        avg_edge_latency = 0
        if metrics['edge_latency']:
            avg_edge_latency = sum(metrics['edge_latency'].values()) / len(metrics['edge_latency'])
        
        return {
            'stream_duration_minutes': stream_duration / 60,
            'current_viewers': metrics['viewers'],
            'peak_viewers': metrics['peak_viewers'],
            'viewer_growth_rate': viewer_growth_rate,
            'transcoding_latency_ms': metrics['transcoding_latency'],
            'average_edge_latency_ms': avg_edge_latency,
            'current_bitrate_mbps': metrics['current_bitrate'],
            'quality_levels_active': len(metrics['quality_levels']),
            'performance_score': self._calculate_performance_score(metrics)
        }
    
    def _calculate_performance_score(self, metrics: Dict[str, any]) -> float:
        """Calculate overall performance score (0-100)"""
        scores = []
        
        # Latency score (lower is better)
        if metrics['transcoding_latency'] > 0:
            latency_score = max(0, 100 - (metrics['transcoding_latency'] / 10))  # Normalize to 1000ms
            scores.append(latency_score)
        
        # Viewer engagement score
        if metrics['peak_viewers'] > 0:
            engagement_score = min(100, (metrics['viewers'] / metrics['peak_viewers']) * 100)
            scores.append(engagement_score)
        
        # Bitrate consistency score
        if metrics['current_bitrate'] > 0:
            # Assume target bitrate and calculate consistency
            bitrate_score = 85  # Placeholder for actual calculation
            scores.append(bitrate_score)
        
        return sum(scores) / len(scores) if scores else 50

class VideoTranscodingOptimizer:
    def __init__(self):
        self.transcoding_profiles = {
            '240p': {'width': 426, 'height': 240, 'bitrate': 400, 'fps': 30},
            '360p': {'width': 640, 'height': 360, 'bitrate': 800, 'fps': 30},
            '480p': {'width': 854, 'height': 480, 'bitrate': 1500, 'fps': 30},
            '720p': {'width': 1280, 'height': 720, 'bitrate': 3000, 'fps': 30},
            '1080p': {'width': 1920, 'height': 1080, 'bitrate': 6000, 'fps': 30},
            '1440p': {'width': 2560, 'height': 1440, 'bitrate': 12000, 'fps': 30},
            '4K': {'width': 3840, 'height': 2160, 'bitrate': 25000, 'fps': 30}
        }
        self.encoding_presets = {
            'ultrafast': {'speed': 10, 'quality': 3},
            'superfast': {'speed': 8, 'quality': 4},
            'veryfast': {'speed': 6, 'quality': 5},
            'faster': {'speed': 4, 'quality': 6},
            'fast': {'speed': 3, 'quality': 7},
            'medium': {'speed': 2, 'quality': 8},
            'slow': {'speed': 1, 'quality': 9}
        }
        
    async def optimize_transcoding_settings(self, stream_config: Dict[str, any],
                                          performance_data: Dict[str, any]) -> Dict[str, any]:
        """Optimize transcoding settings based on performance data"""
        current_viewers = performance_data.get('current_viewers', 0)
        transcoding_latency = performance_data.get('transcoding_latency_ms', 0)
        
        # Determine optimal quality levels based on viewer count
        optimal_qualities = self._select_optimal_qualities(current_viewers)
        
        # Adjust encoding preset based on latency requirements
        optimal_preset = self._select_encoding_preset(transcoding_latency)
        
        # Calculate hardware requirements
        hardware_requirements = self._calculate_hardware_requirements(
            optimal_qualities, optimal_preset
        )
        
        return {
            'recommended_qualities': optimal_qualities,
            'encoding_preset': optimal_preset,
            'hardware_requirements': hardware_requirements,
            'expected_latency_improvement': self._estimate_latency_improvement(
                optimal_preset, stream_config.get('current_preset', 'medium')
            ),
            'quality_settings': {
                quality: self.transcoding_profiles[quality]
                for quality in optimal_qualities
            }
        }
    
    def _select_optimal_qualities(self, viewer_count: int) -> List[str]:
        """Select optimal quality levels based on viewer count and capacity"""
        if viewer_count < 100:
            return ['480p', '720p', '1080p']
        elif viewer_count < 1000:
            return ['360p', '480p', '720p', '1080p']
        elif viewer_count < 10000:
            return ['240p', '360p', '480p', '720p', '1080p']
        else:
            return ['240p', '360p', '480p', '720p', '1080p', '1440p']
    
    def _select_encoding_preset(self, current_latency_ms: float) -> str:
        """Select encoding preset to optimize latency vs quality trade-off"""
        if current_latency_ms > 5000:  # Very high latency
            return 'ultrafast'
        elif current_latency_ms > 3000:  # High latency
            return 'superfast'
        elif current_latency_ms > 2000:  # Moderate latency
            return 'veryfast'
        elif current_latency_ms > 1000:  # Acceptable latency
            return 'faster'
        else:  # Low latency
            return 'fast'

Performance Monitoring and Analytics

Real-Time Quality Metrics

Streaming Quality Assessment:

from collections import deque

from typing import Dict, List, Optional


class StreamingQualityMonitor:
    def __init__(self):
        self.quality_metrics = {}
        self.viewer_sessions = {}
        self.alert_thresholds = {
            'buffer_ratio': 0.05,  # 5% maximum buffer time
            'startup_time': 3000,  # 3 seconds maximum startup
            'quality_switches': 5,  # Maximum quality switches per session
            'error_rate': 0.01     # 1% maximum error rate
        }
        
    async def track_session_quality(self, session_id: str, 
                                  quality_event: Dict[str, any]):
        """Track quality metrics for individual viewer session"""
        if session_id not in self.viewer_sessions:
            self.viewer_sessions[session_id] = {
                'start_time': time.time(),
                'quality_events': deque(maxlen=1000),
                'buffer_events': deque(maxlen=100),
                'error_events': deque(maxlen=50),
                'quality_switches': 0,
                'total_watch_time': 0,
                'total_buffer_time': 0
            }
        
        session = self.viewer_sessions[session_id]
        event_time = time.time()
        
        # Process different types of quality events
        event_type = quality_event['type']
        
        if event_type == 'quality_change':
            await self._handle_quality_change(session, quality_event, event_time)
        elif event_type == 'buffer_start':
            await self._handle_buffer_start(session, quality_event, event_time)
        elif event_type == 'buffer_end':
            await self._handle_buffer_end(session, quality_event, event_time)
        elif event_type == 'playback_error':
            await self._handle_playback_error(session, quality_event, event_time)
        elif event_type == 'session_end':
            await self._handle_session_end(session, quality_event, event_time)
        
        # Check for quality alerts
        await self._check_quality_alerts(session_id, session)
    
    async def _handle_quality_change(self, session: Dict, 
                                   event: Dict[str, any], 
                                   event_time: float):
        """Handle adaptive bitrate quality changes"""
        session['quality_switches'] += 1
        session['quality_events'].append({
            'timestamp': event_time,
            'type': 'quality_change',
            'from_quality': event.get('from_quality'),
            'to_quality': event.get('to_quality'),
            'reason': event.get('reason', 'adaptive')
        })
    
    async def _handle_buffer_start(self, session: Dict, 
                                 event: Dict[str, any], 
                                 event_time: float):
        """Handle buffer start event"""
        session['buffer_events'].append({
            'timestamp': event_time,
            'type': 'buffer_start',
            'position': event.get('position', 0)
        })
    
    async def _handle_buffer_end(self, session: Dict, 
                               event: Dict[str, any], 
                               event_time: float):
        """Handle buffer end event"""
        # Find corresponding buffer start
        buffer_starts = [e for e in session['buffer_events'] if e['type'] == 'buffer_start']
        
        if buffer_starts:
            start_event = buffer_starts[-1]  # Most recent buffer start
            buffer_duration = event_time - start_event['timestamp']
            session['total_buffer_time'] += buffer_duration
            
            session['buffer_events'].append({
                'timestamp': event_time,
                'type': 'buffer_end',
                'duration_ms': buffer_duration * 1000,
                'position': event.get('position', 0)
            })
    
    async def generate_quality_report(self, time_window: int = 3600) -> Dict[str, any]:
        """Generate comprehensive quality report"""
        current_time = time.time()
        cutoff_time = current_time - time_window
        
        # Filter sessions within time window
        recent_sessions = {
            sid: session for sid, session in self.viewer_sessions.items()
            if session['start_time'] > cutoff_time
        }
        
        if not recent_sessions:
            return {'error': 'No sessions in specified time window'}
        
        # Calculate aggregate metrics
        total_sessions = len(recent_sessions)
        
        # Buffer ratio calculation
        total_watch_time = sum(s['total_watch_time'] for s in recent_sessions.values())
        total_buffer_time = sum(s['total_buffer_time'] for s in recent_sessions.values())
        avg_buffer_ratio = total_buffer_time / total_watch_time if total_watch_time > 0 else 0
        
        # Quality switch analysis
        quality_switches = [s['quality_switches'] for s in recent_sessions.values()]
        avg_quality_switches = statistics.mean(quality_switches) if quality_switches else 0
        
        # Error rate calculation
        total_errors = sum(len(s['error_events']) for s in recent_sessions.values())
        error_rate = total_errors / total_sessions if total_sessions > 0 else 0
        
        # Startup time analysis
        startup_times = []
        for session in recent_sessions.values():
            first_play_event = next(
                (e for e in session['quality_events'] if e['type'] == 'play_start'),
                None
            )
            if first_play_event:
                startup_time = first_play_event['timestamp'] - session['start_time']
                startup_times.append(startup_time * 1000)  # Convert to ms
        
        avg_startup_time = statistics.mean(startup_times) if startup_times else 0
        
        # Quality distribution
        quality_distribution = {}
        for session in recent_sessions.values():
            for event in session['quality_events']:
                if event['type'] == 'quality_change':
                    quality = event['to_quality']
                    quality_distribution[quality] = quality_distribution.get(quality, 0) + 1
        
        return {
            'time_window_hours': time_window / 3600,
            'total_sessions': total_sessions,
            'quality_metrics': {
                'average_buffer_ratio': avg_buffer_ratio,
                'average_startup_time_ms': avg_startup_time,
                'average_quality_switches': avg_quality_switches,
                'error_rate': error_rate
            },
            'quality_distribution': quality_distribution,
            'performance_grade': self._calculate_performance_grade({
                'buffer_ratio': avg_buffer_ratio,
                'startup_time': avg_startup_time,
                'quality_switches': avg_quality_switches,
                'error_rate': error_rate
            }),
            'recommendations': self._generate_recommendations({
                'buffer_ratio': avg_buffer_ratio,
                'startup_time': avg_startup_time,
                'quality_switches': avg_quality_switches,
                'error_rate': error_rate
            })
        }
    
    def _calculate_performance_grade(self, metrics: Dict[str, float]) -> str:
        """Calculate overall performance grade A-F"""
        score = 0
        
        # Buffer ratio score (0-25 points)
        if metrics['buffer_ratio'] <= 0.02:
            score += 25
        elif metrics['buffer_ratio'] <= 0.05:
            score += 20
        elif metrics['buffer_ratio'] <= 0.10:
            score += 15
        else:
            score += 0
        
        # Startup time score (0-25 points)
        if metrics['startup_time'] <= 1000:
            score += 25
        elif metrics['startup_time'] <= 2000:
            score += 20
        elif metrics['startup_time'] <= 3000:
            score += 15
        else:
            score += 0
        
        # Quality switches score (0-25 points)
        if metrics['quality_switches'] <= 2:
            score += 25
        elif metrics['quality_switches'] <= 5:
            score += 20
        elif metrics['quality_switches'] <= 10:
            score += 15
        else:
            score += 0
        
        # Error rate score (0-25 points)
        if metrics['error_rate'] <= 0.005:
            score += 25
        elif metrics['error_rate'] <= 0.01:
            score += 20
        elif metrics['error_rate'] <= 0.02:
            score += 15
        else:
            score += 0
        
        # Convert to letter grade
        if score >= 90:
            return 'A'
        elif score >= 80:
            return 'B'
        elif score >= 70:
            return 'C'
        elif score >= 60:
            return 'D'
        else:
            return 'F'

Conclusion

Proxy infrastructure plays a vital role in modern video streaming and CDN optimization, enabling seamless content delivery across global audiences. By implementing the strategies and architectures outlined in this guide, streaming platforms can achieve superior performance, reduce costs, and provide exceptional user experiences.

The key to success lies in understanding the unique challenges of video delivery - from adaptive bitrate streaming to geographic load balancing - and designing proxy solutions that address bandwidth optimization, latency reduction, and quality consistency.

Ready to optimize your video streaming infrastructure? Contact our streaming specialists for customized CDN and proxy solutions designed for your specific requirements, or explore our video-optimized proxy services built for high-performance content delivery.