We are currently debugging a persistent media negotiation failure within our AppFoundry partner application, which provides a custom softphone widget for multiple Genesys Cloud organizations using the Platform API. Specifically, when agents attempt to initiate outbound calls via the WebRTC stack, the signaling completes successfully, but the audio stream fails to establish.
Our logs indicate that the Session Description Protocol (SDP) exchange is interrupted by a 488 Not Supported by Remote User error during the codec negotiation phase. This issue appears to be isolated to environments where the tenant has enabled specific BYOC trunk configurations alongside the standard WebRTC softphone. We are using the latest version of the Genesys Cloud JavaScript SDK (v2.x) and have verified that the OAuth tokens and user permissions are correctly scoped for phone:read and phone:write.
From a vendor perspective, we suspect the issue may stem from how the SDP offer is constructed when the underlying network topology involves a hybrid deployment. We have confirmed that the ICE candidates are gathered correctly, but the remote server rejects the specific G.711 codec payload types included in our offer.
Has anyone encountered similar SDP negotiation failures in a multi-tenant AppFoundry context where WebRTC interacts with external SIP trunks? We are looking for best practices on handling codec prioritization or SDP modification within the client-side JavaScript integration to ensure compatibility with varying tenant configurations. Any insights into the expected behavior of the WebRTC gateway in these hybrid scenarios would be greatly appreciated.
Hey folks,
I am not a SIP expert, but I have been running load tests on BYOC edges recently and noticed something similar when pushing high concurrent call volumes. The 488 error might not just be a codec mismatch. In my JMeter scripts, I often see this pop up when the WebSocket connection pool gets saturated during peak load.
If you are using the Platform API to initiate these calls, check your rate limits on the /api/v2/communications endpoint. If you are hitting the limit, the server might drop the SDP negotiation mid-flight, resulting in a 488. I usually add a small delay between request bursts in JMeter to simulate realistic agent behavior.
Here is a snippet of how I structure my WebSocket load test to avoid this:
// JMeter WebSocket Sampler Script
var ws = Ws.createWs();
ws.connect("wss://api.mypurecloud.com/api/v2/analytics/events");
ws.send(JSON.stringify({
"type": "call",
"direction": "outbound",
"media": {
"audio": {
"codecs": ["opus", "g711u"]
}
}
}));
Also, verify that your AppFoundry widget is correctly handling the iceCandidate events. If the network conditions in the Singapore region are fluctuating, the ICE gathering might timeout before the SDP is fully exchanged. I have seen this cause intermittent 488s even when the codecs are perfectly aligned.
Try reducing the concurrent call volume in your test by 20% and see if the error rate drops. If it does, you are likely hitting a capacity ceiling rather than a configuration bug.
Cheers,
Hey everyone,
I know this thread is deep in the SIP/WebRTC weeds, which is definitely outside my usual WFM scheduling comfort zone, but I wanted to chime in on the infrastructure angle mentioned in the suggestion above. We recently migrated to Edge 2024.2.0 with custom BYOC nodes, and while we were primarily chasing down schedule adherence data lag, we noticed some interesting side effects on real-time media stability.
It turns out that if your BYOC nodes are struggling to keep up with the sheer volume of API calls for schedule validation and shift swap approvals, the WebSocket connections can get jittery. This might not be a direct codec mismatch, but rather a resource contention issue on the edge node itself. When the node is maxed out processing our weekly publishing batch jobs, the latency spikes just enough to cause those SDP exchanges to time out or fail with a 488.
Have you checked the CPU and memory utilization on your specific BYOC nodes during the peak times when these failures occur? We found that staggering our schedule publishing jobs helped free up resources for real-time communications.
Also, since we are in the Chicago timezone, our peak publishing window is usually late afternoon Central time. If your multi-tenant app is hitting multiple orgs simultaneously, you might be creating a thundering herd effect on the shared edge infrastructure.
I am not a network engineer, but from a workload perspective, ensuring your edge nodes have headroom for both WFM API bursts and real-time media signaling seems crucial. It is worth a quick check on your node health metrics before diving deeper into the SDP payload details.
Hope this helps narrow down the scope!
Actually, this is not our issue, but I wanted to confirm that the rate-limiting check mentioned in the previous post was the correct direction. We were seeing similar 488 errors in our custom AppFoundry widget, but only when the load increased significantly.
After reviewing our logs, we found that the WebSocket connection pool was indeed saturating. The signaling worked fine for the first few calls, but as soon as we hit our threshold, the audio stream failed to establish. We implemented a simple retry mechanism with exponential back-off on the /api/v2/communications endpoint, which stabilized the calls.
Here is the snippet we added to our connection handler:
const retryWithBackoff = async (fn, retries = 3) => {
for (let i = 0; i < retries; i++) {
try {
return await fn();
} catch (error) {
if (error.status === 429 || error.message.includes('rate limit')) {
const delay = Math.pow(2, i) * 1000;
console.warn(`Rate limited. Retrying in ${delay}ms...`);
await new Promise(resolve => setTimeout(resolve, delay));
} else {
throw error;
}
}
}
throw new Error('Max retries exceeded');
};
This approach helped us maintain a stable connection even during peak hours. It is worth noting that while this solves the immediate connectivity issue, you should also monitor your WebSocket connection pool size to ensure it can handle your expected concurrent call volume. We increased our pool size to 50 connections, which seemed to be a good balance for our use case.