PalC engineers implement CDN and streaming platforms at the protocol and infrastructure level - Nginx streaming config, Kubernetes HPA for viewer-demand scaling, Prometheus QoE instrumentation, and Anycast BGP routing for edge traffic steering.
Streaming Server - Nginx RTMP to HLS Live Ingest
Live ingest pipeline: RTMP ingest → HLS packaging → CDN delivery
RTMP ingest on media server, transcoded to multiple ABR renditions via FFmpeg, packaged as LL-HLS fragments pushed to edge cache - sub-2s glass-to-glass latency target.
# Nginx-RTMP - live ingest + HLS packaging
rtmp { server { listen 1935; application live { live on; hls on; hls_path /var/hls; hls_fragment 500ms;
# LL-HLS hls_playlist_length 6s; exec ffmpeg -i rtmp://localhost/live/$name -c:v libx264 -b:v 4000k -s 1920x1080 -c:v libx264 -b:v 1500k -s 1280x720 -c:v libx264 -b:v 600k -s 640x360 rtmp://localhost/hls/$name; } } }IngestRTMP / SRTOutputLL-HLS / CMAFLatency<2s glass-to-glassABR1080p / 720p / 360p
Traffic Engineering - Nginx Consistent Hash Load Balancing
Cache-friendly request routing to edge nodes
Consistent hashing routes requests for the same content to the same cache node - maximising cache hit rate while health checks remove failed nodes without cache stampede.
# Nginx upstream - consistent hash by URI
upstream cdn_edge { hash $uri consistent; keepalive 64; server edge-1.cdn.internal:8080 weight=10 max_fails=3 fail_timeout=30s; server edge-2.cdn.internal:8080 weight=10 max_fails=3 fail_timeout=30s; server edge-3.cdn.internal:8080 weight=10 max_fails=3 fail_timeout=30s; } # Signed URL validation at edge - token authAlgorithmConsistent hashHealthActive + passiveAuthSigned URL / JWTLBNginx / Envoy
Orchestration - Kubernetes HPA for Streaming Services
Auto-scaling edge streaming pods on active viewer count
Custom HPA metric from Prometheus - active_stream_sessions drives pod scaling, keeping edge nodes within target capacity before viewer surge causes buffering.
# HPA - scale on active stream sessions
apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
spec:
scaleTargetRef: { kind: Deployment, name: edge-stream-server }
minReplicas: 3
maxReplicas: 50
metrics:
- type: Pods
pods:
metric: { name: active_stream_sessions }
target: { type: AverageValue, averageValue: "200" }
# Scale-up before buffer events occurMetricActive sessionsTriggerPrometheus customScale3-50 podsCooldownTuned per workload
Observability - QoE Prometheus Metrics Instrumentation
Quality-of-Experience metrics exposed per stream session
Per-session QoE counters - rebuffering events, startup time, bitrate switches, and error rate - instrumented at the streaming server and scraped by Prometheus for Grafana QoE dashboards.
// QoE metrics - Prometheus instrumentation (Go)
var (
rebufferCount = prometheus.NewCounterVec(
prometheus.CounterOpts{ Name: "stream_rebuffer_events_total" },
[]string{"stream_id", "region"},
)
startupLatency = prometheus.NewHistogramVec(
prometheus.HistogramOpts{
Name: "stream_startup_seconds",
Buckets: []float64{0.5, 1, 2, 3, 5},
},
[]string{"stream_id", "codec"},
)
)
// Grafana QoE dashboard auto-provisionedMetricsRebuffer - StartupScrapePrometheusDashboardGrafana QoEAlertingSLO-based
