How Edge Computing is Revolutionizing Live Streaming and OTT Video Delivery

Audiences have been losing interest in television for a while now. Many people are replacing linear broadcasting with live streaming and video-on-demand and choosing online streaming services as their main platform for enjoying their favorite entertainment.

In fact, 28.5% of people use the internet to watch live streams, and 82.5% of worldwide internet traffic is dedicated to video streaming. But, just because audiences no longer want to watch TV, they still expect TV quality from their live video streams. Streaming and OTT platforms constantly feel the pressure to deliver seamless, high-definition video to audiences in different parts of the world.

However, the constantly increased consumption of online video strains networks and servers leads to occasional issues like latency, buffering, and stuttering. As viewers come to expect content in 4K and 8K, live streaming platforms are looking for a low-latency streaming technology solution that delivers high quality and no delays.

The solution is called edge computing. This decentralized approach to data processing is making the live-streaming OTT video experience even better for audiences. With edge computing, data processing happens closer to the viewer, cutting down on latency and scalability issues that affect streaming quality.

Let’s learn more about how edge computing improves video streaming. We’ll also discuss what is the role of edge computing in OTT platforms and how it’s set to change the industry for the better.

Table of Contents:

• Edge Computing: The Ins And Outs
• The Limitations of Traditional CDN-Based Streaming
• How Edge Computing Improves Live Streaming and OTT Video Delivery
• How to Integrate Edge Computing With Existing Streaming Infrastructure
• Use Cases of Edge Computing For Live Video Streaming and OTT
• The Future of Edge Computing in Video Streaming
• The Future of Streaming Starts at the Edge

Edge Computing: The Ins And Outs

Let’s start by explaining edge computing. Usually, after generating it, data is sent to a centralized server for processing and further distribution. Sometimes, this centralized location is far away from the end user, and it takes a while to reach them.

Because physical servers take up a lot of space and incur maintenance costs, most streaming services and platforms rely on cloud computing. This means using centralized data centers to process the data and distribute the content. While this solution is flexible and scalable, it also comes with issues like network congestion, high latency, and bandwidth limitations, all of which can be potential live video killers.

In contrast, edge computing processes all the data closer to the data source, i.e., the “edge” of the network. This can mean on the device itself, a nearby server, or a local network node. The proximity of the source means the data has less distance to travel, and processing happens faster, reducing latency considerably.

So which is better, cloud vs edge computing for streaming? Edge computing can complement cloud computing, both working to improve live streaming and OTT content. For example, the cloud can handle storage and content management, while edge servers perform tasks like encoding and edge caching for video delivery to local viewers.

Edge Computing in Media and Entertainment: Key Advantages

Video content is successful if it can grab and keep the attention of your audience. It doesn’t matter whether you’re using a dedicated platform or your own website to stream as long as it’s smooth and clear. Consumers of live video are especially sensitive to lag and buffering, and even a 1% increase in the buffering ratio can lead to a noticeable decline in viewership. The key benefits of edge computing in streaming address this very issue and include:

• Reduced Latency: Edge computing reduces the distance data travels for processing, making that entire process much faster and more efficient.
  
• Lower Bandwidth Costs: Edge computing processes the data locally, reducing the amount of data transmitted and lowering bandwidth costs.
  
• Scalability: Edge networks scale more effectively during peak demand periods. They provide better load balancing and handling of live events with high concurrent viewership, like sports matches.
  
• Improved Content Delivery: By caching content closer to viewers, edge computing improves streaming quality and reduces buffering.

The Limitations of Traditional CDN-Based Streaming

Content delivery networks, or live streaming CDNs, are a staple in streaming and OTT video delivery. They are networks of distributed servers that deliver content to users based on geographic proximity. When a user requests a video, the CDN signals the closest server to deliver it. The content’s quality usually depends on the quality of the CDN. For example, Dacast uses top-tier CDNs like Akamai and Limelight.

Still, despite changing and improving the way we distribute content, CDNs have a few setbacks:

• Latency Issues for Global-Scale Video Delivery: Video streams often travel long distances between the content source and the viewer. It’s what causes the delays and is problematic for live events that depend on real-time broadcasting.
  
• Network Congestion Affecting Video Quality: During high-demand events, more users try to access the video content simultaneously. This overwhelms the CDN servers, leading to packet loss, buffering, and declining video quality.
  
• Video Platforms Incurring High Bandwidth Costs: Due to the large amount of data transferred, serving video content from centralized cloud servers is expensive. Video streaming platforms must pay significant fees to distribute video files globally, especially in high-definition.
  
• Limited Scalability During Peak Events: During massive live events, like the Super Bowl, for example, the demand for content surges exponentially. The centralized CDN infrastructure may struggle to handle the increase in traffic, resulting in slow delivery times or even service outages.

Knowing all this, we can ask the question: Can edge computing replace CDNs for video streaming? Edge computing reduces latency by processing data on devices or at local edge nodes rather than in distant centralized data centers. This helps ease congestion and lower providers’ bandwidth costs. Edge computing can also handle traffic spikes by distributing the load across a vast network of localized nodes.

So, edge computing vs CDN for streaming: Which one emerges victorious? While CDNs are here to stay for the long term, edge computing can improve and extend their capabilities.  That’s why it’s rapidly gaining traction in the industry.

How Edge Computing Improves Live Streaming and OTT Video Delivery

Content providers know that trends in the OTT and live streaming sphere come and go, but the one constant is quality. Demands for high-quality live and VOD content continue to rise, putting pressure on OTT providers to find new ways to deliver buffer-free viewing experiences. Using edge computing for OTT and live video delivery provides a solution for the common issues of latency, low resolution, and scalability.

Lower Latency And Faster Video Delivery

Latency can really impact the user experience when it comes to video streaming. For example, Twitch streams have a latency of 10-15 seconds, which is enough time for users to look at the live chat and get spoilers about what’s coming up next. Edge computing can lower the delay from reduced latency (18-6 seconds) to low latency (6-2 seconds).

Depending on network conditions and internet speed, some users can even experience ultra-low latency (2-0.2 seconds). This raises the question: How does edge computing reduce latency in live streaming? 

The answer – with real-time video processing at the edge of the network that is closest to the end user. The proximity reduces transmission delays because the data travels less to reach the viewers.

Edge computing also affects the adaptive bitrate. It helps the ABR algorithm operate faster by dynamically adjusting the bitrate in real time. This results in smoother video playback, especially in fluctuating network conditions.

Reduced Buffering And Improved Quality

One of the biggest issues online audiences face is buffering. If users spend more than 0.4 of their total time watching their video buffer, your platform will see a 30% decline in view time and even a decline in viewership. Edge computing resolves this problem by caching data close to the viewer, which decreases buffering.

Caching means storing frequently accessed content near the user. The main servers don’t need to retrieve data from a distant centralized server, reducing their load. The caching mechanism is particularly valuable for high-demand live events where audiences expect near-instant content delivery.

Audiences also expect high-definition video content from their streaming and OTT providers. Let’s see how edge computing improves 4K and 8K streaming quality. Thanks to data caching, it almost removes the delays in video transmission and optimizes bandwidth usage.

It also allows for more efficient video encoding and compression processes, which results in smaller file sizes, even for 4K and 8K videos, reducing the strain on networks. For example, the 2020 Tokyo Olympics wanted to deliver 8K coverage of the games worldwide. The NHK used local edge servers to stream the events without losing video quality or overwhelming the global internet backbone.

Better Load Balancing And Scalability

In addition to VODs and short-form video content, people are increasingly live-streaming massive events. The 2025 Super Bowl stream had a peak audience of 15.5 million concurrent views, putting a massive strain on the platform’s capabilities. This is where edge computing and live streaming truly come together.

With edge computing, live streaming platforms can distribute the processing load more efficiently. They can also scale video delivery to accommodate constantly growing global audiences. Decentralized edge networks have multiple nodes that share the burden of processing and delivering data.

The load is balanced across several servers, preventing any single server from overloading. Even if viewership continues to increase, the stream quality remains the same for the duration of the event.

Bandwidth Cost Optimization

Delivering high-definition video globally is bandwidth-intensive and expensive. Since the edge network’s nodes are closer to the end user, the overall demand for more bandwidth is lower, lowering costs as well. Using edge computing can lead to noticeable cost savings for video streaming providers, even during high-demand events with large audiences.

Enhancing Personalized Video Experiences

Edge AI for video streaming allows AI algorithms to run locally and process viewer data in real time. The content recommendations those viewers get are based on their personal viewing patterns and preferences, giving them a highly personalized and instant experience.

That means content providers also receive real-time analytics on viewership behavior, which can help them recognize patterns or fix potential issues before they affect the stream.

Increased Reliability And Uptime

In centralized cloud architectures, an outage in the main server can affect users across the entire platform. Edge computing spreads the workload across many edge nodes, so even if one node fails, the remaining nodes will pick up the slack, and users won’t notice any issues.

This is especially important for events like live gaming and online auctions, where even the slightest delay can be disastrous.

How to Integrate Edge Computing With Existing Streaming Infrastructure

OTT platforms and live streaming providers that want to improve their performance and cost-efficiency should seriously consider implementing edge computing into their existing infrastructure.

Best practices for implementing edge computing in video streaming include assessing your current streaming architecture and making the necessary hardware and software adjustments. Start by evaluating your server locations, network capacity, CDN performance, and video delivery quality.

Then, you can identify optimal locations for edge nodes near end-users in high-demand regions. We recommend gradually adapting your existing CDNs and edge computing using a hybrid model to get the best of both worlds. The edge servers will handle caching, encoding, and real-time processing, offloading work from centralized servers and reducing network congestion.

You also have to consider hardware, software, and development solutions.

Hardware Requirements:

Software Requirements:

Development and Deployment Solutions

How Much Will Edge Computing Cost

So, how much will these edge integration cost providers? Let’s take a look at some estimated costs:

*Please note that these are general estimates, and costs will depend on your chosen solutions and the scope of operations.

Challenges and Considerations in Implementing Edge Computing for Streaming

While edge computing has great potential, as we can see, implementing it is not a small or cheap task. Some other challenges you might face, include:

• Infrastructure And Deployment: Deploying edge nodes across multiple geographical locations requires robust infrastructure and logistical planning.
  
• Cost Considerations: Service providers must balance the initial cost of edge computing integration with long-term savings and performance improvements.
  
• Security Concerns: Due to the distributed nature of the edge network, edge computing may introduce potential security and privacy risks.
  
• Scalability Considerations: As your OTT platform grows, the edge network must also scale up. The edge infrastructure can handle increasing demand during peak times.

Use Cases of Edge Computing For Live Video Streaming and OTT

Now that we know what edge computing is capable of, let’s examine how OTT platforms use it to improve their services and offerings to audiences. This technology addresses the growing demand for high-quality, uninterrupted live entertainment, like:

• Live Sports And Esports Streaming: We already mentioned how the Super Bowl and the Tokyo Olympics use edge computing to drastically reduce lag by processing video feeds in real time. Riot Games uses the same concept when gamers across the globe stream and play Valorant.
• Cloud Gaming and Interactive Streaming: Cloud gaming allows players to play resource-intensive games in real-time. These games usually have interactive elements, such as player text and voice chat, for in-game collaborations. Edge computing provides real-time responsiveness without lag or stutter.
• OTT Platforms And VOD Services: Top OTT platforms and VOD services can use edge technology to handle massive data flows without compromising on stream quality. It will drastically reduce buffering times and let viewers start the most-watched content almost instantly.
• VR And AR Streaming: Edge computing significantly reduces motion-to-photon latency, quickly adjusting visual content in response to user actions. This decreases the discomfort and motion sickness that sometimes happens when using this technology.
• Smart TV And Mobile Streaming: Implementing 5G edge computing in video streaming makes smart TVs and mobile devices more capable of handling high-definition video content in areas with limited network resources.

The Future of Edge Computing in Video Streaming

Currently, the global edge computing market is worth $60 billion, but this number will surpass $110 billion in 2029. Such a rapid increase means users can expect better capabilities and functionalities from their live stream and OTT providers.

Edge computing also has great potential when used alongside 5G networks and AI. 5G’s ultra-fast speeds and low latency, edge servers can process massive amounts of data concurrently. AI can give personalized content recommendations, optimize streaming quality, and predict and mitigate potential issues.

Thanks to multi-access edge computing, live streaming can also be further advanced. MEC deploys computing resources at the network edge and at multiple points of presence across a distributed network. This means that streams of large-scale events like concerts and global conferences never suffer from interruptions because the network load is evenly allocated.

Dacast: Your Premier Choice For OTT Streaming

Dacast provides a powerful platform that simplifies video delivery without compromising performance for companies looking to integrate edge computing into their OTT and live streaming strategy. Its end-to-end streaming solutions support everything from video hosting and content management to OTT monetization, security, and global distribution.

By combining our CDN efficiency with edge computing capabilities, you can reduce buffering and improve stream stability. You can even deliver content in high definition with almost zero lag.

For companies looking to customize their streaming experience, Dacast offers API-driven integration and white-label solutions. This means businesses can:

• Build their own branded streaming apps and platforms.
• Integrate Dacast’s streaming capabilities into existing workflows.
• Maintain full control over the user experience without third-party branding.

The Future of Streaming Starts at the Edge

When the slightest lag or delay in broadcasting can mean losing millions of viewers simultaneously, edge computing stands as the solution to slow loading, buffering, and other stream interruptions.

For broadcasters and video platforms, investing in edge-powered streaming solutions is more than a simple upgrade. It will keep you competitive as viewer expectations continue to rise. With its ultra-low latency streaming, scalable global delivery, and cost-efficient infrastructure, incorporating edge computing in streaming and OTT content unlocks new possibilities for providers.

If you’re looking for a live streaming or OTT platform that can seamlessly integrate with the latest edge technology, Dacast has what you need. If you’re ready to optimize your streaming performance, try our 14-day free trial now – no card required!

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