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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Scale Wowza Streaming Engine by finding the workload bottleneck, then adding the capacity that addresses it: edge servers and load balancing for viewer delivery, HTTP Origin with a cache or CDN for HTTP playback, or more processing capacity when transcoding is the constraint. There is no dependable universal viewers-per-server figure. Test your actual streams, protocols, bitrates, transcoding profile, and audience behavior before an event.
Start by identifying what needs to scale
A live-stream deployment can run short of capacity in different places. Adding servers without locating the bottleneck can increase cost and operational complexity without fixing the viewer problem.
- Stream processing: The Engine is struggling to ingest, package, or transcode streams.
- Origin ingest: The server receiving encoder feeds is overloaded or is the point of failure.
- Viewer delivery: Too many player connections or requests are reaching the delivery server.
- Network capacity: The server or network path cannot carry the required incoming or outgoing traffic.
Before choosing an architecture, inventory concurrent source streams and their bitrates, expected peak viewers, viewer geography, playback protocols, and whether Wowza will transcode. Include the possibility of an audience spike, and decide what should happen if an origin or edge becomes unavailable. Wowza’s scaling guidance says capacity depends on hardware, network configuration, stream type, bitrate, and connection type; the reliable way to find a deployment’s limit is to load test that configuration.
How many viewers can one Wowza server handle?
There is no single viewer count that applies to every Wowza server. The same server can have different practical limits depending on the workload and network, and transcoding can add processing demands beyond serving already encoded streams. Treat any published figure as meaningful only when its hardware, workload, protocols, and test conditions match yours.
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Wowza documentation includes cluster scenarios labelled “up to 50,000 concurrent connections” and above 50,000 for a larger-cluster scenario. These are scenario labels, not a per-server rating or a guarantee for a particular deployment. Use load testing, not those labels or a generic rule of thumb, to set your own capacity target.
Choose an architecture for the workload
| Situation | Starting point | Main trade-off |
|---|---|---|
| One stream and a modest, predictable audience | One Engine instance, monitored and load tested | Simplest to operate; capacity is limited to what that tested configuration can handle. |
| A large audience served by Wowza servers | One or more repeater origins, edge servers, and load balancing | Adds delivery capacity and control, but requires routing and cluster operations. |
| Mostly HLS or MPEG-DASH playback, especially for a bursty event | HTTP Origin with a cache or CDN | Can offload HTTP delivery; requires a compatible workflow, configuration, and cost review. |
| A short event that needs temporary Wowza capacity | Cloud Engine instances used as event edges | Avoids keeping peak capacity online year-round; runtime and setup still need budgeting. |
| Viewers on a controlled private network with configurable routers | UDP multicast, if the network supports it | Routers provide fan-out; it is not a general solution for public-internet audiences. |
Build a Wowza origin-edge deployment
In a repeater design, encoders publish to an origin and edge servers serve viewers. The origin may also transcode. Edges reduce repeated work at the origin by obtaining streams from it and serving player connections; add edge capacity as demand grows, and use a load-balancing method to distribute player connections among edges.
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- Decide what the origin will do. Identify whether it only receives and provides source streams or will also handle transcoding. Keep delivery and transcoding capacity in the plan as separate potential constraints.
- Configure the edge-to-origin relationship. Wowza’s current repeater setup documentation supports a primary origin and an optional secondary origin. The secondary is for failover if the primary fails; the documented client wait can be up to 12 seconds before failover to the backup origin completes. This is documented behavior, not a guarantee for every network or client.
- Choose repeater mode deliberately. For the trade-offs between on-demand and persistent connections, see the next section.
- Put player traffic behind load balancing. Distribute connections across available edges, and configure the balancer to detect and remove failed servers. A well-designed balancer should make it practical to add or remove servers as capacity needs change.
- Test failover as well as normal load. Confirm what happens when an origin or edge is unavailable, how quickly the balancer stops sending it players, and whether the remaining servers can carry the resulting traffic.
On-demand or persistent repeater mode?
| Mode | How it works | Useful when | Trade-off |
|---|---|---|---|
| On-demand | An edge requests a stream when a player asks for it. This is the default for Live Edge applications. | You want a simpler setup that can scale as viewers request streams. | Connections disconnect when no longer requested. HTTP segments are packetized on the origin, so the same segment sequence is available at edges and player migration can be seamless. |
| Persistent | Configured streams connect in advance, using Stream Files or Java API methods. | Streams should be available on an edge before viewers request them, or multiple protocols are requested continuously over a long period. | It may use less bandwidth over a long period of continuous multi-protocol requests. HTTP segments may differ between edges, which can interrupt playback if a player moves between them. |
Neither mode is universally better. Choose based on stream availability requirements, protocol mix, bandwidth, player migration behavior, and the operational work your deployment can support.
Use HTTP Origin with a cache or CDN for HTTP playback
In normal HTTP playback sessions, unique session IDs prevent HTTP proxies from caching the content. Wowza HTTP Origin mode is sessionless, enabling caching proxies to cache and redistribute content. For HTTP workflows such as HLS and MPEG-DASH, this can reduce requests reaching Engine when viewer connections exceed what one server can handle.
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Wowza names Amazon CloudFront as a ready-built caching proxy that can sit in front of an Engine server and scale automatically; it also identifies third-party CDN options including Akamai and EdgeCast. Its developer documentation also describes a Wowza CDN on Fastly workflow: stream directly to that CDN from a Live HTTP Origin application, or send a stream to Wowza Video for transcoding and CDN delivery. These are different service and workflow choices; confirm current availability, configuration requirements, and pricing with the provider.
- Check that the playback protocol and application workflow are compatible with the intended cache path.
- Consider where viewers are located and whether the CDN can serve them effectively.
- Compare setup effort, origin protection, event duration, and service cost for your expected traffic.
- Do not assume a CDN is always cheaper. Wowza notes that CloudFront may be costly for long periods of heavy use in one cluster scenario; actual cost depends on the workload and current provider pricing.
Keep transcoding capacity separate from delivery capacity
Wowza Transcoder decodes incoming audio and video, then re-encodes outputs for target devices. Aligned keyframes in output renditions support adaptive-bitrate delivery. If transcoding is the bottleneck, adding edges that only distribute already encoded output may not address it; plan and test the transcoding work separately from viewer delivery.
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Wowza’s current Transcoder guide says video scaling uses CPU by default. Supported NVIDIA CUDA and AMD Xilinx U30 acceleration options can reduce CPU use for scaling, subject to platform and configuration limits; AMD Xilinx U30 acceleration is Linux-only. A GPU identifier setting does not itself provide automatic GPU load balancing. Choose acceleration only after checking the deployed Engine version, platform, supported configuration, and measured workload—these documents do not establish a GPU model or performance gain for a particular deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Load-test the deployment you intend to run
Wowza’s scaling documentation identifies load testing as the way to determine when a particular configuration is likely to degrade, and its documentation index points to a load-testing guide using Apache JMeter. Neither establishes a universal benchmark or a standard load profile for every deployment.
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- Reproduce the real stream workload. Use representative codecs, bitrates, protocols, source-stream count, transcoding settings, and viewer geography.
- Model viewer behavior. Include realistic connection ramps and playback behavior rather than relying only on a steady-state connection count.
- Increase load in stages. Record the load level at which performance begins to degrade instead of testing only one target.
- Measure the whole path. Watch server resource use and network throughput, and record startup time, buffering, and errors as load rises.
- Test failure conditions. Remove an edge or origin during a test, and check load-balancer detection, backup-origin behavior, and the capacity of the remaining servers.
- Set capacity with operating margin. Base the target on the measured degradation point and expected event variation, not a vendor scenario label.
Budget for peak demand, not just server count
An always-on cluster sized for peak attendance keeps that capacity online between events. Cloud instances can instead add temporary edge capacity for a burst and be removed when demand falls, but setup and runtime still cost money. For either approach, include the cost of load balancing, CDN or cache delivery where used, and any separate transcoding capacity in the deployment estimate. The sources cited here do not establish current cloud or CDN prices; calculate them against your expected duration, traffic, region, and configuration.
Troubleshoot common scaling failures
- One server remains overloaded after adding edges: Check whether the overloaded host is the origin, a transcoding server, or the network path rather than an edge serving viewers. Move capacity to the actual bottleneck and test again.
- Viewers still concentrate on one edge: Review the load balancer’s routing and health detection, confirm that player connections are being distributed, and verify that failed edges are removed from rotation.
- Playback breaks when viewers move between edges: Check whether persistent mode is producing different HTTP segment sequences on different edges. Evaluate on-demand mode, where the origin packetizes HTTP segments and edges have the same sequence, against your availability and bandwidth needs.
- HTTP delivery continues to burden Engine: Confirm that the playback workflow is using HTTP Origin mode and that the caching proxy or CDN is configured to cache and redistribute the content. Session-based HTTP playback is not cacheable through ordinary proxies in the way HTTP Origin is designed to enable.
- Failover takes longer than expected: Account for the documented possibility of clients waiting up to 12 seconds for a secondary-origin failover, then test with your actual client and network path. Do not treat that documented maximum as a universal recovery guarantee.
- Adding a GPU does not reduce the expected workload: Verify supported platform and configuration, and remember that setting a GPU identifier does not configure automatic GPU load balancing. Measure the Transcoder workload and CPU use in the actual deployment.
- The system passes a small test but struggles at the event: Compare the test with the real codecs, bitrates, protocols, transcoding profile, viewer geography, and ramp pattern. A test that omits a material part of the workload does not establish capacity for the event.
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