Loss-aware streaming throttling 2025 — AVIF/HEIC bandwidth control with quality SLOs

High-compression formats such as AVIF and HEIC slash bandwidth, but they are also more fragile: re-encoding and CDN latency can erode visual fidelity. At tens of thousands of concurrent viewers you must keep quality SLOs while policing throughput. This playbook explains how to implement loss-aware streaming throttling, monitor quality targets, and roll back safely—written for web engineers shipping modern image delivery pipelines.

TL;DR

• Throttling policy: compute max_bandwidth = (region_bandwidth × 0.8) - priority_traffic and map users into HD, SD, Fallback slots.
• Quality SLOs: enforce ΔSSIM < 0.03, ΔVMAF < 2, ΔLCP < 120 ms; if breached, immediately down-shift quality.
• Adaptive payloads: retire HTTP/2 Push; negotiate formats with Priority Hints plus the Accept header.
• Signal routing: pipe loss_bucket out of edge logs, monitor in Prometheus/Grafana, and auto-demote to JPEG/PNG if thresholds trip.
• CI/CD integration: use image-quality-budgets-ci-gates to measure ΔSSIM/ΔPSNR during builds and alert on risky changes.

Throttling architecture

Calculating bandwidth slots

// edge/throttle.ts
export function assignSlot({ regionBandwidth, priorityTraffic, currentUsers }: {
  regionBandwidth: number
  priorityTraffic: number
  currentUsers: number
}) {
  const max = Math.max(0, regionBandwidth * 0.8 - priorityTraffic)
  const perUser = max / Math.max(currentUsers, 1)
  if (perUser >= 350_000) return 'HD' // AVIF 2x
  if (perUser >= 180_000) return 'SD' // WebP fallback
  return 'Fallback' // Progressive JPEG
}

Switching formats at the edge

// workers/image-router.js
addEventListener('fetch', event => {
  event.respondWith(handle(event.request))
})

async function handle(request) {
  const slot = assignSlot(await getRegionMetrics(request))
  const accept = request.headers.get('Accept') || ''

  if (slot === 'HD' && accept.includes('image/avif')) {
    return fetchAsset(request, 'avif')
  }
  if (slot !== 'Fallback' && accept.includes('image/heic')) {
    return fetchAsset(request, 'heic')
  }
  return fetchAsset(request, 'jpeg')
}

Tune fetchAsset to adjust Cache-Control per slot so lower-fidelity variants expire faster.

Monitoring quality

Collect field data via the performance-guardian agent and track the quality SLOs continuously.

sendToGuardian('image_quality', {
  deltaSSIM,
  deltaVMAF,
  slot,
  userAgent,
  throughput: navigator.connection?.downlink || 0,
  lcp: getCurrentLCP()
})

Define the SLO in Grafana/Prometheus like this:

slo:
  name: image-delivery-loss
  target: 99.5
  window: 7d
  indicator:
    ratio:
      success_metric: sum(rate(image_quality_good[5m]))
      total_metric: sum(rate(image_quality_total[5m]))

image_quality_good counts events where ΔSSIM/ΔVMAF stay within bounds. When the SLO is violated enforce slot = Fallback automatically.

Quality checks in CI

Configure image-quality-budgets-ci-gates to guard regressions before rollout.

{
  "budgets": [
    {
      "pattern": "public/images/**/*.avif",
      "compareWith": "baseline",
      "thresholds": {
        "ssim": 0.03,
        "vmaf": 2,
        "psnr": 1.5
      }
    }
  ]
}

If the diff exceeds thresholds, the build fails and posts to Slack for review.

Rollback strategy

• Automated rollback: when the HD slot failure rate exceeds 30%, immediately demote to SD and re-evaluate every two minutes.
• Human review: the quality team verifies the compare slider artifacts for visual regressions.
• CDN purge: after forcing the fallback tier, purge the affected variants using surrogate-key.

A/B testing and user impact

Streaming throttling changes UX. Validate the blast radius with experimentation.

Checklist

• [ ] Region bandwidth and priority traffic feed the slot assignment logic.
• [ ] Field ΔSSIM/ΔVMAF data is collected and stored.
• [ ] SLO alerts reach the SRE rotation.
• [ ] AVIF/HEIC quality deltas are validated in CI.
• [ ] CDN purge workflow is automated for rollbacks.
• [ ] UX metrics are monitored via A/B tests.

Summary

Loss-aware streaming throttling is how you pair bandwidth savings with quality guarantees. Combine real-time bandwidth telemetry with quality SLOs, and wire automation for format switching plus rollbacks. With the guardrails in place you can serve massive traffic spikes while keeping image fidelity steady. Design delivery logic and observability together so that your team catches and resolves degradation before users notice.