Understanding the Economics of Transcoding

Understanding the Economics of Transcoding

Whether your business model is FAST or subscription-based premium content, your success depends upon your ability to deliver a high-quality viewing experience while relentlessly reducing costs. Transcoding is one of the most expensive production-related costs and the ultimate determinant of video quality, so obviously plays a huge role on both sides of this equation. This article identifies the most relevant metrics for ascertaining the true cost of transcoding and then uses these metrics to compare the relative cost of the available methods for live transcoding.

Economics of Transcoding: Cost Metrics

There are two potential cost categories associated with transcoding: capital costs and operating costs. Capital costs arise when you buy your own transcoding gear, while operating costs apply when you operate this equipment or use a cloud provider. Let’s discuss each in turn.

Economics of Transcoding: CAPEX

The simplest way to compare transcoders is to normalize capital and operating costs using the cost per stream or cost per ladder, which simplifies comparing disparate systems with different costs and throughput. The cost per stream applies to services inputting and delivering a single stream, while the cost per ladder applies to services inputting a single stream and outputting an encoding ladder.

We’ll present real-world comparisons once we introduce the available transcoding options, but for the purposes of this discussion, consider the simple example in Table 1. The top line shows that System B costs twice as much as System A, while line 2 shows that it also offers 250% of the capacity of System A. On a cost-per-stream basis, System B is actually cheaper.

Understanding the Economics of Transcoding - table 1
TABLE 1: A simple cost-per-stream analysis.

The next few lines use this data to compute the number of required systems for each approach and the total CAPEX. Assuming that your service needs 640 simultaneous streams, the total CAPEX for System A dwarfs that of System B. Clearly, just because a particular system costs more than another doesn’t make it the more expensive option.

For the record, the throughput of a particular server is also referred to as density, and it obviously impacts OPEX charges. System B delivers over six times the streams from the same 1RU rack as System A, so is much more dense, which will directly impact both power consumption and storage charges.

Details Matter

Several factors complicate the otherwise simple analysis of cost per stream. First, you should analyze using the output codec or codecs, current and future. Many systems output H.264 quite competently but choke considerably with the much more complex HEVC codec. If AV1 may be in your future plans, you should prioritize a transcoder that outputs AV1 and compare cost per stream against all alternatives.

The second requirement is to use consistent output parameters. Some vendors quote throughput at 30 fps, some at 60 fps. Obviously, you need to use the same value for all transcoding options. As a rough rule of thumb, if a vendor quotes 60 fps, you can double the throughput for 30 fps, so a system that can output 8 1080p60 streams and likely output 16 1080p30 streams. Obviously, you should verify this before buying.

If a vendor quotes in streams and you’re outputting encoding ladders, it’s more complicated. Encoding ladders involve scaling to lower resolutions for the lower-quality rungs. If the transcoder performs scaling on-board, throughput should be greater than systems that scale using the host CPU, and you can deploy a less capable (and less expensive) host system.

The last consideration involves the concept of “operating point,” or the encoding parameters that you would likely use for your production, and the throughput and quality at those parameters. To explain, most transcoders include encoding options that trade off quality vs throughput much like presets do for x264 and x265. Choosing the optimal setting for your transcoding hardware is often a balance of throughput and bandwidth costs. That is, if a particular setting saves 10% bandwidth, it might make economic sense to encode using that setting even if it drops throughput by 10% and raises your capital cost accordingly. So, you’d want to compute your throughput numbers and cost per stream at that operating point.

In addition, many transcoders produce lower throughput when operating in low latency mode. If you’re transcoding for low-latency productions, you should ascertain whether the quoted figures in the spec sheets are for normal or low latency.

For these reasons, completing a thorough comparison requires a two-step analysis. Use spec sheet numbers to identify transcoders that you’d like to consider and acquire them for further testing. Once you have them in your labs you can identify the operating point for all candidates, test at these settings, and compare them accordingly.

Economics of Transcoding: OPEX - Power

Now, let’s look at OPEX, which has two components: power and storage costs. Table 2 continues our example, looking at power consumption.

Unfortunately, ascertaining power consumption may be complicated if you’re buying individual transcoders rather than a complete system. That’s because while transcoding manufacturers often list the power consumption utilized by their devices, you can only run these devices in a complete system. Within the system, power consumption will vary by the number of units configured in the system and the specific functions performed by the transcoder.

Note that the most significant contributor to overall system power consumption is the CPU. Referring back to the previous section, a transcoder that scales onboard will require lower CPU contribution than a system that scales using the host CPU, reducing overall CPU consumption. Along the same lines, a system without a hardware transcoder uses the CPU for all functions, maxing out CPU utilization likely consuming about the same energy as a system loaded with transcoders that collectively might consume 200 watts. 

Again, the only way to achieve a full apples-to-apples comparison is to configure the server as you would for production and measure power consumption directly. Fortunately, as you can see in Table 2, stream throughput is a major determinant of overall power consumption. Even if you assume that systems A and B both consume the same power, System B’s throughput makes it much cheaper to operate over a five year expected life, and much kinder to the environment.

Understanding the Economics of Transcoding - table 2
TABLE 2. Computing the watts per stream of the two systems.

Economics of Transcoding: Storage Costs

Once you purchase the systems, you’ll have to house them. While these costs are easiest to compute if you’re paying for a third-party co-location service, you’ll have to estimate costs even for in-house data centers. Table 3 continues the five year cost estimates for our two systems, and the denser system B proves much cheaper to house as well as power.

Understanding the Economics of Transcoding - table 3
TABLE 3: Computing the storage costs for the two systems.

Economics of Transcoding: Transcoding Options

These are the cost fundamentals, now let’s explore them within the context of different encoding architectures.

There are three general transcoding options: CPU-only, GPU, and ASIC-based. There are also FPGA-based solutions, though these will probably be supplanted by cheaper-to-manufacture ASIC-based devices over time. Briefly,

  • CPU-based transcoding, also called software-based transcoding, relies on the host central processing unit, or CPU, for all transcoding functions.
  • GPU-based transcoding refers to Graphic Processing Units, which are developed primarily for graphics-related functions but may also transcode video. These are added to the server in add-in PCIe cards.
  • ASICs are Application-Specific Integrated Circuits designed specifically for transcoding. These are added to the server as add-in PCIe cards or devices that conform to the U.2 form factor.

Economics of Transcoding: Real-World Comparison

NETINT manufactures ASIC-based transcoders and video processing units. Recently, we published a case study where a customer, Mayflower, rigorously and exhaustively compared these three alternatives, and we’ll share the results here.

By way of background, Mayflower’s use case needed to input 10,000 incoming simultaneous streams and distribute over a million outgoing simultaneous streams worldwide at a latency of one to two seconds. Mayflower hosts a worldwide service available 24/7/365.

Mayflower started with 80-core bare metal servers and tested CPU-based transcoding, then GPU-based transcoding, and then two generations of ASIC-based transcoding. Table 4 shows the net/net of their analysis, with NETINT’s Quadra T2 delivering the lowest cost per stream and the greatest density, which contributed to the lowest co-location and power costs.

RESULTS: COST AND POWER

Understanding the Economics of Transcoding - table 4
TABLE 4. A real-world comparison of the cost per stream and OPEX associated with different transcoding techniques.

As you can see, the T2 delivered an 85% reduction in CAPEX with ~90% reductions in OPEX as compared to CPU-based transcoding. CAPEX savings as compared to the NVIDIA T4 GPU was about 57%, with OPEX savings around ~70%.

Table 5 shows the five-year cost of the Mayflower T-2 based solution using the cost per KWH in Cyprus of $0.335. As you can see, the total is $2,225,241, a number we’ll return to in a moment.

Understanding the Economics of Transcoding - table 5
TABLE 5: Five-year cost of the Mayflower transcoding facility.

Just to close a loop, Tables 1, 2, and 3, compare the cost and performance of a Quadra Video Server equipped with ten Quadra T1U VPUs (Video Processing Units) with CPU-based transcoding on the same server platform. You can read more details on that comparison here.

Table 6 shows the total cost of both solutions. In terms of overall outlay, meeting the transcoding requirements with the Quadra-based System B costs 73% less than the CPU-based system. If that sounds like a significant savings, keep reading. 

TABLE 6: Total cost of the CPU-based System A and Quadra T2-based System B.

Economics of Transcoding: Cloud Comparison

If you’re transcoding in the cloud, all of your costs are OPEX. With AWS, you have two alternatives: producing your streams with Elemental MediaLive or renting EC3 instances and running your own transcoding farm. We considered the MediaLive approach here, and it appears economically unviable for 24/7/365 operation.

Using Mayflower’s numbers, the CPU-only approach required 500 80-core Intel servers running 24/7. The closest CPU in the Amazon ECU pricing calculator was the 64-core c6i.16xlarge, which, under the EC2 Instance Savings plan, with a 3-year commitment and no upfront payment, costs 1,125.84/month.

Understanding the Economics of Transcoding - figure 1
FIGURE 1. The annual cost of the Mayflower system if using AWS.

We used Amazon’s pricing calculator to roll these numbers out to 12 months and 500 simultaneous servers, and you see the annual result in Figure 1. Multiply this by five to get to the five-year cost of $33,775,056, which is 15 times the cost of the Quadra T2 solution, as shown in table 5.

We ran the same calculation on the 13 systems required for the Quadra Video Server analysis shown in Tables 1-3 which was powered by a 32-core AMD CPU. Assuming a c6a.8xlarge CPU with a 3-year commitment and no upfront payment,, this produced an annual charge of $79,042.95, or $395,214.6 for the five-year period, which is about 8 times more costly than the Quadra-based solution.

Understanding the Economics of Transcoding - figure 2
FIGURE 2: The annual cost of an AWS system per the example schema presented in tables 1-3.

Cloud services are an effective means for getting services up and running, but are vastly more expensive than building your own encoding infrastructure. Service providers looking to achieve or enhance profitability and competitiveness should strongly consider building their own transcoding systems. As we’ve shown, building a system based on ASICs will be the least expensive option.

In August, NETINT held a symposium on Building Your Own Live Streaming Cloud. The on-demand version is available for any video engineer seeking guidance on which encoder architecture to acquire, the available software options for transcoding, where to install and run your encoding servers, and progress made on minimizing power consumption and your carbon footprint.

ON-DEMAND: Building Your Own Live Streaming Cloud

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The LESS Accord and Energy-Efficient Streaming

The goal of our recent Build Your Live Streaming Cloud symposium was to help live video engineers learn how to build and house their own transcoding infrastructure while minimizing power consumption and carbon footprint. Accordingly, we invited Barbara Lange from the Greening of Streaming to speak at the symposium. This article relates the key points of her talk, particularly describing the short-term goals of the Low Energy Sustainable Streaming (LESS) Accord.

By way of background, Barbara is a Volunteer Secretariat for the Greening of Streaming and the principal and CEO of Kibo121, a consultancy dedicated to guiding the media tech sector towards sustainability. Barbara described the Greening of Streaming as a member organization formed roughly two years ago. Its primary focus is on the end-to-end energy efficiency of the technical supply chain that supports streaming services.

The organization has an international membership and is dedicated to addressing the energy implications of the streaming sector. Their mission is to provide the global internet streaming industry with a platform to enhance engineering practices and promote collaboration throughout the supply chain. One core belief is that as streaming increases in scope, understanding the true energy costs, backed by real-world data, is paramount. Barbara mentioned that the organization’s monthly membership meetings are now open to the public, with the next meeting scheduled for October 11 at 11:00 Eastern. 

Barbara then described the organization’s structure, highlighting its nine current working groups, which focus on diverse pursuits like defining terminology, organizing industry outreach, and identifying best practices. One notable initiative was the measurement of energy consumption during an English Premier soccer match. The organization also explores power consumption in audio streaming, compression/decompression, and the standardization of energy data.

A newly formed group is dedicated to understanding the energy costs associated with end-user devices. Barbara emphasized the importance of collaboration with academic and other industry groups to avoid duplication of effort and to ensure consistent and effective communication across the industry.

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LESS ACCORD

With this as background, Barbara focused on the LESS Accord. She began by addressing a common misconception, which is that contrary to some media reports, there’s almost no direct correlation between internet traffic, measured in gigabytes, and energy consumption, measured in kilowatt-hours. This realization emerged from discussions within Working Group Six, which is responsible for examining compression-related issues. This group initiated the LESS Accord.

The LESS Accord’s mission statement is to define best practices for employing compression technologies in streaming video workflows. The goal is to optimize energy efficiency while ensuring a consistently high-quality viewing experience for users. These guidelines target energy reduction throughout the entire streaming process, from the initial encoding for distribution to the decoding and display on consumer devices for all video delivery services.

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As Barbara reported, over the past six months, the group has actively engaged with industry professionals, engineers, and experts. They’ve sought insights and suggestions on how to enhance energy efficiency across all workflow and system stages. The essence of the Accord is to foster a collaborative environment where various, sometimes contrasting, initiatives from recent years can be harmonized.

The ultimate goal is to refine testing objectives and pinpoint organizations that can form project groups. Barbara detailed the first of four projects designated in the LESS Accord’s mission statement.

PROJECT ONE: INTELLIGENT DISTRIBUTION MODEL SHIFTING

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Project one involves is determining the most energy-efficient distribution model at any given time and enabling content delivery networks (CDNs) to seamlessly transition between these models. The three distribution models to be considered are:

  • Unicast: The dominant model in today’s internet streaming.
  • Peer-to-peer: Typically used for video on demand distribution.
  • Net layer multicast: Often deployed for IPTV.

While each model has traditionally served a specific purpose, the group believes that all three could be viable options in various contexts. The hypothesis is that if these models can be provisioned almost spontaneously, there should be an underlying heuristic that facilitates the shift from one model to another. If energy efficiency is the primary concern, this shift could allow the CDN to meet that objective.

The main goal of this project is to design a workflow that incorporates energy measurements for the involved systems. The aim is to discern when an operator should transition from one model to another, with energy consumption of the entire system being the primary driver, without compromising the end user’s experience.

PROJECT TWO: THE "GOOD ENOUGH" CONCEPT

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Barbara then described the second project, which involves potential energy savings through codec choices and optimization. The central question is whether energy can be conserved by allowing consumers to opt for a streaming experience that prioritizes energy efficiency.

The concept suggests introducing a “green button” on streaming media player devices or applications. By pressing this button, users would choose an experience optimized for energy conservation. Drawing a parallel, Barbara mentioned that many televisions come equipped with an “ECO” mode, which many users tend to disable or overlook. Project two will explore whether consumers might be more inclined to select the energy-efficient option if the energy consumption differences between modes were better communicated.

Taking the idea further, this project will explore consumer behavior if the devices defaulted to this ECO or green mode, and users had the choice to upgrade to a “gold mode” for a potentially enhanced quality. Or, if the default setting prioritized energy efficiency, would this lead to a more energy-conserving streaming system?

The project aims to explore these questions, especially considering that many users currently avoid ECO modes, possibly due to perceived concerns about service quality. As you’ve read, this project seeks to understand user behavior and preferences in the context of energy-efficient streaming.

PROJECT THREE: ENERGY MEASUREMENT THROUGHOUT WORKFLOWS

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Barbara then described the third project, which she acknowledged as particularly intricate. The central challenge is to measure energy consumption at every stage of the streaming workflow. This initiative originated from Working Group Four, which has been exploring methods to monitor and probe systems to determine the energy costs associated with each step of the process.

The overarching question is: how much energy is required to deliver a stream to the consumer? While answering this question would be invaluable for economic, marketing, and feedback purposes, it’s a complex endeavor.

The proposed approach involves tracking energy consumption from start to finish in the streaming process. When a video file is created on a computer and encoding begins, an energy reading in kilowatt-hours could be taken. This process would be repeated at each subsequent production, delivery, and playback stage. The idea is to tag the video file with “energy breadcrumbs” or metadata that gets updated as the file progresses through the workflow. By the end, these breadcrumbs would provide a comprehensive view of the energy costs associated with the entire streaming process.

Barbara emphasized the ambitious nature of this project, noting that while it’s uncertain if they can fully realize this vision, they are committed to exploring it. She believes that this project, if successful, could have the most significant impact in terms of understanding energy consumption in the streaming sector.

PROJECT FOUR: TRANSITIONING WORKFLOWS FOR ENERGY EFFICIENCY

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Barbara introduced the fourth project, which will explore how to adapt various technologies to transition existing workflows to hardware environments that are more energy-efficient. Some initial areas of exploration include:

  • Optimization between different silicon environments: Examining how different hardware platforms can be more energy-efficient.
  • Immersion cooling: Comparing traditional air cooling systems with alternative cooling methods in streaming environments. This includes processes like encoding, packaging, caching, and even playback in consumer electronics.
  • Deploying tasks to renewable energy infrastructures: Specifically, relocating non-time-sensitive encoding tasks to infrastructures powered by surplus renewable energy. An exciting development in this area is the interest shown by the Scottish Enterprise which aims to test the relocation of non-critical transcoding workloads to a wind-powered facility in Scotland.

ENERGY-EFFICIENT STREAMING

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Barbara emphasized that all these projects were established during a Greening of Streaming event in June, and are currently in progress. She invited interested parties to join these projects and announced an upcoming member meeting that was held on September 13. Next one – October 11th.

Additionally, at IBC in September, the Greening of Streaming plans to present these projects to a broader audience, kick off the work in the fourth quarter, and continue into the next year. By the NAB event in April 2024, the organization hopes to discuss the projects in-depth and share test results.

ON-DEMAND: Barbara Lange - Empowering a Greener Tomorrow:
The LESS Accord and its Energy Savings Drive

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Choosing Transcoding Hardware: Deciphering the Superiority of ASIC-based Technology

Which technology reigns supreme in transcoding: CPU-only, GPU, or ASIC-based? Kenneth Robinson’s incisive analysis from the recent symposium makes a compelling case for ASIC-based transcoding hardware, particularly NETINT’s Quadra. Robinson’s metrics prioritized viewer experience, power efficiency, and cost. While CPU-only systems appear initially economical, they falter with advanced codecs like HEVC. NVIDIA’s GPU transcoding offers more promise, but the Quadra system still outclasses both in quality, cost per stream, and power consumption. Furthermore, Quadra’s adaptability allows a seamless switch between H.264 and HEVC without incurring additional costs. Independent assessments, such as Ilya Mikhaelis‘, echo Robinson’s conclusions, cementing ASIC-based transcoding hardware as the optimal choice.

Choosing transcoding hardware

During the recent symposium, Kenneth Robinson, NETINT’s manager of Field Application Engineering, compared three transcoding technologies: CPU-only, GPU, and ASIC-based transcoding hardware. His analysis, which incorporated quality, throughput, and power consumption, is useful as a template for testing methodology and for the results. You can watch his presentation here and download a copy of his presentation materials here.

Figure 1. Overall savings from ASIC-based transcoding (Quadra) over GPU (NVIDIA) and CPU.
Figure 1. Overall savings from ASIC-based transcoding (Quadra) over GPU (NVIDIA) and CPU.

As a preview of his findings, Kenneth found that when producing H.264, ASIC-based hardware transcoding delivered CAPEX savings of 86% and 77% compared to CPU and GPU-based transcoding, respectively. OPEX savings were 95% vs. CPU-only transcoding and 88% compared to GPU.

For the more computationally complex HEVC codec, the savings were even greater. As compared to CPU-based transcoding, ASICs saved 94% on CAPEX and 98% on OPEX. As compared to GPU-based transcoding, ASICs saved 82% on CAPEX and 90% on OPEX. These savings are obviously profound and can make the difference between a successful and profitable service and one that’s mired in red ink.

Let’s jump into Kenneth’s analysis.

Determining Factors

Digging into the transcoding alternatives, Kenneth described the three options. First are CPUs from manufacturers like AMD or Intel. Second are GPUs from companies like NVIDIA or AMD. Third are ASICs, or Application Specific Integrated Circuits, from manufacturers like NETINT. Kenneth noted that NETINT calls its Quadra devices Video Processing Units (VPU), rather than transcoders because they perform multiple additional functions besides transcoding, including onboard scaling, overlay, and AI processing.

He then outlined the factors used to determine the optimal choice, detailing the four factors shown in Figure 2. Quality is the average quality as assessed using metrics like VMAF, PSNR, or subjective video quality evaluations involving A/B comparisons with viewers. Kenneth used VMAF for this comparison. VMAF has been shown to have the highest correlation with subjective scores, which makes it a good predictor of viewer quality of experience.

Choosing transcoding hardware - Determining Factors
Figure 2. How Kenneth compared the technologies.

Low-frame quality is the lowest VMAF score on any frame in the file. This is a predictor for transient quality issues that might only impact a short segment of the file. While these might not significantly impact overall average quality, short, low-quality regions may nonetheless degrade the viewer’s quality of experience, so are worth tracking in addition to average quality.

Server capacity measures how many streams each configuration can output, which is also referred to as throughput. Dividing server cost by the number of output streams produces the cost per stream, which is the most relevant capital cost comparison. The higher the number of output streams, the lower the cost per stream and the lower the necessary capital expenditures (CAPEX) when launching the service or sourcing additional capacity.

Power consumption measures the power draw of a server during operation. Dividing this by the number of streams produced results in the power per stream, the most useful figure for comparing different technologies.

Detailing his test procedures, Kenneth noted that he tested CPU-only transcoding on a system equipped with an AMD Epic 32-core CPU. Then he installed the NVIDIA L4 GPU (a recent release) for GPU testing and NETINT’s Quadra T1U U.2 form factor VPU for ASIC-based testing.

He evaluated two codecs, H.264 and HEVC, using a single file, the Meridian file from Netflix, which contains a mix of low and high-motion scenes and many challenging elements like bright lights, smoke and fog, and very dark regions. If you’re testing for your own deployments, Kenneth recommended testing with your own test footage.

Kenneth used FFmpeg to run all transcodes, testing CPU-only quality using the x264 and x265 codecs using the medium and very fast presets. He used FFmpeg for NVIDIA and NETINT testing as well, transcoding with the native H.264 and H.265 codec for each device.

H.264 Average, Low-Frame, and Rolling Frame Quality

The first result Kenneth presented was average H.264 quality. As shown in Figure 3, Kenneth encoded the Meridian file to four output files for each technology, with encodes at 2.2 Mbps, 3.0 Mbps, 3.9 Mbps, and 4.75 Mbps. In this “rate-distortion curve” display, the left axis is VMAF quality, and the bottom axis is bitrate. In all such displays, higher results are better, and Quadra’s blue line is the best alternative at all tested bitrates, beating NVIDIA and x264 using the medium and very fast presets.

Figure 3. Quadra was tops in H.264 quality at all tested bitrates.
Figure 3. Quadra was tops in H.264 quality at all tested bitrates.

Kenneth next shared the low-frame scores (Figure 4), noting that while the NVIDIA L4’s score was marginally higher than the Quadra’s, the difference at the higher end was only 1%. Since no viewer would notice this differential, this indicates operational parity in this measure.

Figure 4. NVIDIA’s L4 and the Quadra achieve relative parity in H.264 low-frame testing.
Figure 4. NVIDIA’s L4 and the Quadra achieve relative parity in H.264 low-frame testing.

The final H.264 quality finding displayed a 20-second rolling average of the VMAF score. As you can see in Figure 5, the Quadra, which is the blue line, is consistently higher than the NVIDIA L4 or medium or very fast. So, even though the Quadra had a lower single-frame VMAF score compared to NVIDIA, over the course of the entire file, the quality was predominantly superior.

Figure 5. 20-second rolling frame quality over file duration.
Figure 5. 20-second rolling frame quality over file duration.

HEVC Average, Low-Frame, and Rolling Frame Quality

Kenneth then related the same results for HEVC. In terms of average quality (Figure 6), NVIDIA was slightly higher than the Quadra, but the delta was insignificant. Specifically, NVIDIA’s advantage starts at 0.2% and drops to 0.04% at the higher bit rates. So, again, a difference that no viewer would notice. Both NVIDIA and Quadra produced better quality than CPU-only transcoding with x265 and the medium and very fast presets.

Figure 6. Quadra was tops in H.264 quality at all tested bitrates.
Figure 6. Quadra was tops in H.264 quality at all tested bitrates.

In the low-frame measure (Figure 7), Quadra proved consistently superior, with NVIDIA significantly lower, again a predictor for transient quality issues. In this measure, Quadra also consistently outperformed x265 using medium and very fast presets, which is impressive.

Figure 7. NVIDIA’s L4 and the Quadra achieve relative parity in H.264 low-frame testing.
Figure 7. NVIDIA’s L4 and the Quadra achieve relative parity in H.264 low-frame testing.

Finally, HEVC moving average scoring (Figure 8) again showed Quadra to be consistently better across all frames when compared to the other alternatives. You see NVIDIA’s downward spike around frame 3796, which could indicate a transient quality drop that could impact the viewer’s quality of experience.

Figure 8. 20-second rolling frame quality over file duration.
Figure 8. 20-second rolling frame quality over file duration.

Cost Per Stream and Power Consumption Per Stream - H.264

To measure cost and power consumption per stream, Kenneth first calculated the cost for a single server for each transcoding technology and then measured throughput and power consumption for that server using each technology. Then, he compared the results, assuming that a video engineer had to source and run systems capable of transcoding 320 1080p30 streams.

You see the first step for H.264 in Figure 9. The baseline computer without add-in cards costs $7,100 but can only output fifteen 1080p30 streams using an average of the medium and veryfast presets, resulting in a cost per stream was $473. Kenneth installed two NVIDIA L4 cards in the same system, which boosted the price to $14,214, but more than tripled throughput to fifty streams, dropping cost per stream to $285. Kenneth installed ten Quadra T1U VPUs in the system, which increased the price to $21,000, but skyrocketed throughput to 320 1080p30 streams, and a $65 cost per stream.

This analysis reveals why computing and focusing on the cost per stream is so important; though the Quadra system costs roughly three times the CPU-only system, the ASIC-fueled output is over 21 times greater, producing a much lower cost per stream. You’ll see how that impacts CAPEX for our 320-stream required output in a few slides.

Figure 9. Computing system cost and cost per stream.
Figure 9. Computing system cost and cost per stream.

Figure 10 shows the power consumption per stream computation. Kenneth measured power consumption during processing and divided that by the number of output streams produced. This analysis again illustrates why normalizing power consumption on a per-stream basis is so necessary; though the CPU-only system draws the least power, making it appear to be the most efficient, on a per-stream basis, it’s almost 20x the power draw of the Quadra system.

Figure 10. Computing power per stream for H.264 transcoding.
Figure 10. Computing power per stream for H.264 transcoding.

Figure 11 summarizes CAPEX and OPEX for a 320-channel system. Note that Kenneth rounded down rather than up to compute the total number of servers for CPU-only and NVIDIA. That is, at a capacity of 15 streams for CPU-only transcoding, you would need 21.33 servers to produce 320 streams. Since you can’t buy a fractional server, you would need 22, not the 21 shown. Ditto for NVIDIA and the six servers, which, at 50 output streams each, should have been 6.4, or actually 7. So, the savings shown are underrepresented by about 4.5% for CPU-only and 15% for NVIDIA. Even without the corrections, the CAPEX and OPEX differences are quite substantial.

Figure 11. CAPEX and OPEX for 320 H.264 1080p30 streams.
Figure 11. CAPEX and OPEX for 320 H.264 1080p30 streams.

Cost Per Stream and Power Consumption Per Stream - HEVC

Kenneth performed the same analysis for HEVC. All systems cost the same, but throughput of the CPU-only and NVIDIA-equipped systems both drop significantly, boosting their costs per stream. The ASIC-powered Quadra outputs the same stream count for HEVC as for H.264, producing an identical cost per stream.

Figure 12. Computing system cost and cost per stream.
Figure 12. Computing system cost and cost per stream.

The throughput drop for CPU-only and NVIDIA transcoding also boosted the power consumption per stream, while Quadra’s remained the same.

Figure 13. Computing power per stream for H.264 transcoding.
Figure 13. Computing power per stream for H.264 transcoding.

Figure 14 shows the total CAPEX and OPEX for the 320-channel system, and this time, all calculations are correct. While CPU-only systems are tenuous–at best– for H.264, they’re clearly economically untenable with more advanced codecs like HEVC. While the differential isn’t quite so stark with the NVIDIA products, Quadra’s superior quality and much lower CAPEX and OPEX are compelling reasons to adopt the ASIC-based solution.

Figure 14. CAPEX and OPEX for 320 1080p30 HEVC streams.
Figure 14. CAPEX and OPEX for 320 1080p30 HEVC streams.

As Kenneth pointed out in his talk, even if you’re producing only H.264 today, if you’re considering HEVC in the future, it still makes sense to choose a Quadra-equipped system because you can switch over to HEVC with no extra hardware cost at any time. With a CPU-only system, you’ll have to more than double your CAPEX spending, while with NVIDIA,  you’ll need to spend another 25% to meet capacity.

The Cost of Redundancy

Kenneth concluded his talk with a discussion of full hardware and geo-redundancy. He envisioned a setup where one location houses two servers (a primary and a backup) for full hardware redundancy. A similar setup would be replicated in a second location for geo-redundancy. Using the Quadra video server, four servers could provide both levels of redundancy, costing a total of $84,000. Obviously, this is much cheaper than any of the other transcoding alternatives.

NETINT’s Quadra VPU proved slightly superior in quality to the alternatives, vastly cheaper than CPU-only transcoding, and very meaningfully more affordable than GPU-based transcoders. While these conclusions may seem unsurprising – an employee at an encoding ASIC manufacturer concludes that his ASIC-based technology is best — you can check Ilya Mikhaelis’ independent analysis here and see that he reached the same result.

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Reducing Power Consumption in Data Centers: A Response to the European Energy Crisis

Reducing power consumption - European Energy Crisis

Encoding technology refreshes are seldom CFO driven. For European data centers, over the next few years, they may need to be as reducing power consumption in data centers becomes a primary focus.

Few European consumers or businesses need to be reminded that they are in the midst of a power crisis. But a recent McKinsey & Company article entitled Four themes shaping the future of the stormy European power market provides interesting insights into the causes of the crisis and its expected duration. Engineering and technical leaders, don’t stop reading because this crisis will impact the architecture and technology decisions you may be making.

The bottom line, according to McKinsey? Buckle up, Europe, “With the frequency of high-intensity heat waves expected to increase, additional outages of nuclear facilities planned in 2023, and further expected reductions in Russian gas imports, we expect that wholesale power prices may not reduce substantially (defined as returning to three times higher than pre-crisis levels) until at least 2027.” If you haven’t been thinking about steps your organization should take to reduce power consumption and carbon emissions, now is the time.

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HARD QUESTIONS ON HOT TOPICS – EUROPEAN ENERGY CRISIS AS PER MCKINSEY REPORT
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The Past

The war in Ukraine is the most obvious contributor to the energy crisis, but McKinsey identifies multiple additional contributing factors. Significantly, even before the War, Europe was in the midst of “structural challenges” caused by its transition from carbon-emitting fossil fuels to cleaner and more sustainable sources like wind, solar, and hydroelectric.

Then, in 2022, the shock waves began. Prior to the invasion of Ukraine in February, Russia supplied 30 percent of Europe’s natural gas, which dropped by as much as 50% in 2022, and is expected to decline further. This was exacerbated by a drop of 19% in hydroelectric power caused by drought and a 14% drop in nuclear power caused by required maintenance that closed 32 of France’s 56 reactors. As a result, “wholesale prices of both electricity and natural gas nearly quadrupled from previous records in the third quarter of 2022 compared with 2021, creating concerns for skyrocketing energy costs for consumers and businesses.”

Reducing Power Consumption in Europe - NETINT technologies
Figure 1. As most European consumers and businesses know, prices skyrocketed in 2022
and are expected to remain high through 2027 and beyond.

Four key themes

Looking ahead, McKinsey identifies four key themes it expects to shape the market’s evolution over the next five years.

  • Increase in Required Demand

McKinsey sees power usage increasing from 2,900 terawatt-hours (TWh) in 2021 to 3,700 TWh in 2030, driven by multiple factors. For example, the switch to electric cars and other modes of transportation will increase power consumption by 14% annually. In addition, the manufacturing sector, which needs power for electrolysis, will increase to 200 TWh by 2030.

  • The Rise of Intermittent Renewable Energy Sources

By 2030, wind and solar power will provide 60% of Europe’s energy, double the share in 2021. This will require significant new construction but could also face challenges like supply chain issues, material shortages, and a scarcity of suitable land and talent.

  • Balancing Intermittent Energy Sources

McKinsey sees the energy market diverging into two types of sources; intermittent sources like solar, wind, and hydroelectric, and dispatchable sources like coal, natural gas, and nuclear that can be turned on and off to meet peak requirements. Over the next several years, McKinsey predicts that “a gap will develop between peak loads and the dispatchable power capacity that can be switched on to meet it.”

To close the gap, Europe has been aggressively developing clean energy sources of dispatchable capacity, including utility-scale battery systems, biomass, and hydrogen. In particular, hydrogen is set to play a key role in Europe’s energy future, as a source of dispatchable power and as a means to store energy from renewable sources.

All these sources must be further implemented and massively scaled, with “build-outs remaining highly uncertain due to a reliance on supportive regulations, the availability of government incentives, and the need for raw materials that are in short supply, such as lithium ion.”

  • New and evolving markets and rules

Beyond temporary measures designed to reduce costs for energy consumers, European policymakers are considering several options to reform how the EU energy market operates. These include

  • A central buyer model: A single EU or national regulatory agency would purchase electricity from dispatchable sources at fixed prices under long-term contracts and sell it to the market at average cost prices.
  • Decoupled day-ahead markets: Separate zero marginal cost energy resources (wind, solar) and marginal cost resources (coal) into separate markets to prioritize dispatching of renewables.
  • Capacity remuneration mechanism: Grid operator provides subsidies to producers based on forecast cost of keeping power capacity in the market to ensure a steady supply of dispatchable electricity and protect consumers.

McKinsey closes on a positive note, “Although the European power market is experiencing one of its most challenging periods, close collaboration among stakeholders (such as utilities, suppliers, and policy makers) can enable Europe’s green-energy transition to continue while ensuring a stable supply of power.”

The future of the European power market is complex and subject to many challenges, but policymakers and stakeholders are working to address them and find solutions to ensure a stable and affordable energy system for consumers and businesses.

In the meantime, the mandate for data centers isn’t new as video engineers are being asked to reduce power consumption to save OPEX, reduce carbon footprint to ensure ESG metrics are hit by the company, and minimize the potential disruption of energy instability.

If you’re in this mode, NETINT’s ASIC-based transcoders can help by offering the lowest available power draw of any silicon solution (CPU, GPU, FPGA), and thus the highest possible density.

Is power consumption your company’s priority?

Is power consumption your company's priority?

Power consumption is a priority for NETINT customers and a passion for NETINT engineers and technicians. Matthew Ariho, a system engineer in SoC Engineering at NETINT, recently answered some questions about:

  • How to test power consumption
  • Which computer components draw the most power
  • Why using older computers is bad for your power bills, and
  • The best way for video-centric data centers to reduce power consumption.

What are the different ways to test power consumption (and cost)?

Is power consumption your company's priority? - Matthew Ariho
Matthew Ariho

There are software and hardware-based solutions to this problem. I use one of each as a means of confirming any results.

One software tool is the IPMItool linux package which provides a simple command-line interface to IPMI-enabled devices through a Linux kernel driver. This tool polls the instantaneous, average and peak and minimum instantaneous power draw of the over a sampling period.

Is power consumption your company's priority?

On the hardware side of things, you can use different forms of multimeters, like the Kill-A-Watt meter and a 208VAC power bar are examples of such devices available in our lab.

What are their pros and cons (and accuracy)?

Is power consumption your company's priority? - Matthew Ariho
Matthew Ariho

The IPMItool is great because it provides a lot of information. It is fairly simple to set up and use. There is a question of reliability because it is software based, it depends on readings whose source I’m not familiar with.

The multimeters (like the Kill-A-Watt meter), while also simple to use, do not have any logging capabilities which makes measurements like average or steady state power draw difficult to measure. Both methods have a resolution of 1W which is not ideal but more than sufficient for our use cases.

What activities to you run when you test power consumption?

Is power consumption your company's priority? - Matthew Ariho
Matthew Ariho

We run multi-instances that mimic streaming workloads but only to the point that each of those instances is performing up to par with our standards (for example, 30 fps).

What’s the range of power consumption you’ve seen?

Is power consumption your company's priority? - Matthew Ariho
Matthew Ariho

I’ve seen reports of power consumption of up to 450 watts, but personally never tested a unit that drew that much. Typically, without any load on the T408 devices, the power consumption hovers around 150W, which increases to 210 to 220W during peak periods.

What’s the difference between Power Supply rating and actual power consumption (and are they related)?

Is power consumption your company's priority? - Matthew Ariho
Matthew Ariho

Power supplies take in 120VAC or 208VAC and convert to various DC voltages (12V, 5V, 3.3V) to power different devices in a computer. This conversion process inherently has several inefficiencies. The extent of these inefficiencies depends on the make of the power supply and the quality of components used.

Power supplies are offered with an efficiency rating that certify how efficiently a power supply will function at different loads. Power consumption measured at the wall will always be less than power supplied within a computer.

What are the hidden sources of excessive power that most people don’t know about?

Is power consumption your company's priority? - Matthew Ariho
Matthew Ariho

The operating system of a computer can consume a lot of power performing background tasks though this has become less of a problem with more efficient CPUs on the market. Other sources of excessive power are bloatware that are usually unnecessary programs that run in the background.

What distinguishes a power-hungry computer from an efficient one – what should the reader look for?

Is power consumption your company's priority? - Matthew Ariho
Matthew Ariho

The power supply rating is something to watch. Small variations in the power supply rating make significant differences in efficiency. The difference between a PSU rated at 80 PLUS and a PSU rated at 80 PLUS Bronze is about 2% to 5% depending on the load. This number only grows with better rated PSUs.

Other factors including the components of the computer. Recently, newer devices (CPUs, GPUs and motherboards) have been made with beyond significant generational improvements in efficiency. A top-of-the-line computer from 3 years ago simply cannot compete with some mid-range computers in terms of both power efficiency or performance. So, while sourcing older but cheaper components in the past may have been a good decision, nowadays, its not as clear cut.

Which components draw the most power?

Is power consumption your company's priority? - Matthew Ariho
Matthew Ariho

CPUs and GPUs. Even consumer CPUs can draw over 200W sustained. GPUs on the lower end consume around 150W and now more recently over 400W.

How does the number of cores in a computer impact power usage?

Is power consumption your company's priority? - Matthew Ariho
Matthew Ariho

I’m really not an expert on server components and it is hard to say without having examples. There are too many options to provide a conclusion on a proper trend. There are AMD 64 core server CPUs that pull about 250 to 270 W and 12 to 38 core Intel server CPUs that do about the same. Ultimately architectural advantages/features determine performance and efficiencies when comparing CPUs across manufacturer or even CPUs from the same manufacturer.

You can't manage what you don't measure.

- Peter Drucker -

One famous quote attributed to Peter Drucker is that you can’t manage what you don’t measure. As power consumption becomes increasingly important, it’s incumbent upon all of us to both measure and manage it.

2022-Opportunities and Challenges for the Streaming Video Industry

2022-Opportunities and Challenges for the Streaming Video Industry

As 2022 comes to a close, for those in the streaming video industry, it will be remembered as a turbulent year marked by new opportunities, including the emergence of new video platforms and services.

2022 started off with Meta’s futuristic vision of the internet known as the Metaverse. The Metaverse can be described as a combination of virtual reality, augmented reality, and video where users interact within a digital universe. The Metaverse continues to evolve with the trend of unique individual, one-to-one video streaming experiences in contrast to one-to-many video streaming services which are commonplace today. 

Recent surveys have shown that two-thirds of consumers are planning to cut back on streaming subscriptions due to rising costs and diminishing discretionary income. With consumers becoming more value-conscious and price-sensitive, Netflix and other platforms have introduced new innovative subscriber models. Netflix’s subscription offering, in addition to SVOD (Subscription Video on Demand), now includes an Ad-based tier, AVOD (Advertising Video on Demand).  

Netflix shows the way

This new ad-based tier targets the most price sensitive customers and it is projected that AVOD growth will lead SVOD by 3x in 2023. Netflix can potentially earn over $4B in advertising revenue, making them the second largest ad support platform only after YouTube. This year also saw Netflix making big moves into mobile cloud gaming with the purchase of its 6th gaming studio. Adding gaming to their product portfolio serves at least two purposes: it expands the number of platforms that can access their game titles and serves as another service to maintain their existing users.

These new services and platforms are a small sample of the continued growth in new streaming video services where business opportunities abound for video platforms willing to innovate and take risks.

Stop data center expansion

The new streaming video industry landscape requires platforms to provide innovative new services to highly cost sensitive customers in a regulatory environment that discourages data center expansion. To prosper in 2023 and beyond, video platforms must address key issues to prosper and add services and subscribers.

  • Controlling data center sprawl – new services and extra capacity can no longer be contingent on the creation of new and larger data centers.
  • Controlling OPEX and CAPEX – in the current global economic climate, costs need to be controlled to keep prices under control and drive subscriber growth. In addition, in today’s economic uncertainty, access to financing and capital to fund data expansion cannot be assumed.
  • Energy consumption and environmental impact are intrinsically linked, and both must be reduced. Governments are now enacting environmental regulations and platforms that do not adopt green policies do so at their own peril.

Application Specific Integrated Circuit

For a vision of what needs to be done to address these issues, one only needs to glimpse into the recent past at YouTube’s Argos VCU (Video Coding Unit). Argos is YouTube’s in-house designed ASIC (Application Specific Integrated Circuit) encoder that, among other objectives, enabled YouTube to reduce their encoding costs, server footprint, and power consumption. YouTube is encoding over 500 hours (about 3 weeks) of content per minute.

To stay ahead of this workload, Google designed their own ASIC, which enabled them to eliminate millions of Intel CPUs. Obviously, not everyone has their own in-house ASIC development team, but whether you are a hyperscale platform, commercial, institutional, or government video platform, the NETINT Codensity ASIC-powered video processing units are available.

To enable faster adoption, NETINT partnered with Supermicro, the global leader in green server solutions. The NETINT Video Transcoding Server is based on a 1RU Supermicro server powered with 10 NETINT T408 ASIC-based video transcoder modules. The NETINT Video Transcoding Server, with its ASIC encoding engine, enables a 20x reduction in operational costs compared to CPU/software-based encoding. The massive savings in operational costs offset the CAPEX associated with upgrading to the NETINT video transcoding server.

Supermicro and T408 Server Bundle

In addition to the extraordinary cost savings, the advantages of ASIC encoding include enabling a reduction in the server footprint by a factor of 25x or more, which has a corresponding reduction in power consumption and, as a bonus, is also accompanied by a 25x reduction in carbon emissions. This enables video platforms to expand encoding capacity without increasing their server or carbon footprints, avoiding potential regulatory setbacks.

In need of environmentally friendly technologies

2022 has seen the emergence of many new opportunities with the launch of new innovative video services and platforms. To ensure the business success of these services, in the light of global economic uncertainty and geopolitical unrest, video platforms must rethink how these services are deployed and embrace new cost-efficient, environmentally friendly technologies.