Unicast Video Accounts for Most of the Internet Bandwidth Increases We See

Constant and significant increases in bandwidth consumption are among the fateful implications of switching from linear TV broadcasting to multicast video streaming. Consider that video now constitutes 52 percent to 88 percent of all internet traffic. 

Not all that increase is the direct result of video streaming services. Video now is an important part of social media interactions and advertising on web sites supporting consumer applications, though some studies suggest social media sites overall represent only seven percent to about 15 percent of video traffic consumed by end users. 

Also, there is some amount of internet video traffic between data centers, not intended directly for end users, possibly representing five percent of global internet traffic. 

Study	Date	Video Traffic Share (%)
Cisco Annual Internet Report (2023)	Dec 2022	88%
Sandvine Global Internet Phenomena Report (Q3 2023)	Sep 2023	83%
Limelight Networks State of the Real-Time Web Report (Q3 2023)	Oct 2023	76%
Ericsson Mobility Report (Nov 2023)	Nov 2023	72%
ITU Global Video Traffic Forecasts	Feb 2023	70% (2022)
Ookla Global Video Report (Q2 2023)	Aug 2023	65%
Akamai State of the Internet / Security Report (Q3 2023)	Oct 2023	60%
Statista: Global Internet Traffic Distribution by Content Type (2023)	Oct 2023	58%
GlobalWebIndex Social Video Trends Report (Q3 2023)	Sep 2023	55%
Juniper Networks Visual Networking Index (2023)	Feb 2023	52% (2022)

Ignoring for the moment the impact of video resolution on bandwidth consumption (higher resolution requires more bandwidth), the key change is that broadcasting essentially uses a “one-to-many” architecture, while streaming uses a unicast architecture. 

The best example is that a scheduled broadcast TV show, for example, can essentially send one copy of the content to every viewer (multicast or broadcast delivery). The same number of views, using internet delivery, essentially requires sending the same copy to each viewer separately (unicast delivery). 

In other words, 10 homes watching one multicast or broadcast program, on one channel, at one time consumes X amount of network bandwidth. If 10 homes watch a program of the same file size as the broadcast content, whether simultaneously or not, then bandwidth consumption is 10X. 

There are some nuances for real-world data consumption, such as the fact that consumption of linear video is declining or the fact that broadcasting uses a constant amount of bandwidth, no matter how many viewers in an area might be watching or not watching. 

Study	Comparison	Bandwidth Ratio (Streaming/Broadcasting)
"A Comparative Analysis of Video Streaming and Broadcasting for Live Sports Events" (2023)	Live sports streaming vs. multicast	10x - 15x
"Bandwidth Efficiency of IPTV vs. Traditional Broadcasting" (2022)	IPTV unicasting vs. terrestrial broadcasting	2x - 4x
"The Impact of Unicast Video Delivery on Network Traffic" (2021)	Unicasting video vs. multicast video	1.5x - 3x
"Comparing the Bandwidth Consumption of Live Streaming and P2P Delivery" (2020)	Live streaming vs. P2P for live events	3x - 6x
"The Bandwidth Efficiency of Video Streaming Protocols" (2019)	HTTP streaming vs. RTMP streaming	1.2x - 2x
"A Study of User-Generated Video Delivery on Social Media Platforms" (2018)	User-generated video streaming vs. traditional video streaming	2x - 4x
"The Bandwidth Implications of 4K and 8K Video Streaming" (2017)	Higher resolution streaming vs. standard definition	4x - 8x
"The Impact of Mobile Video Streaming on Network Congestion" (2016)	Mobile video streaming vs. fixed-line streaming	1.5x - 3x
"The Future of Video Delivery: A Cost Comparison of Streaming and Broadcasting" (2015)	Streaming vs. broadcasting for future content delivery	2x - 4x
"The Bandwidth Efficiency of Video-on-Demand Services" (2014)	Video-on-demand streaming vs. linear broadcasting	1.5x - 2.5x

There are other nuances as well. Since a broadcast video stream often is viewed on a television set, it is possible that multiple viewers “share” viewing of the same content. If one TV is receiving a program, and five people are watching, the “single delivery” supports five views. 

On a “per viewer” basis, X amount of delivery bandwidth is X/5 for each viewer of the same program. 

If five people watch a program of equivalent file size at the same time, data consumption is 5X. 

Study	Year	Methodology	Streaming Bandwidth (Mbps)	Linear Broadcasting Bandwidth (Mbps)
Nielsen	2022	Network traffic analysis	3.1-4.7 (average)	0.1-0.2 (average)
OpenVault	2023	ISP data analysis	1.8-2.5 (average)	0.05-0.15 (average)
Pew Research Center	2021	Survey and network analysis	2.3-3.8 (average)	0.1-0.2 (average)
University of Zurich	2019	Network monitoring and simulation	2.0-3.5 (average)	0.08-0.18 (average)
Akamai	2020	Global traffic analysis	1.6-2.8 (average)	0.04-0.12 (average)
Sandvine	2022	Network traffic analysis report	3.5-5.0 (peak)	0.15-0.25 (peak)
Netflix	2021	Open Connect content delivery platform report	0.5-1.5 (average)	N/A
BBC Research & Development	2018	HbbTV hybrid broadcasting analysis	1.0-2.0 (combined)	0.03-0.08 (combined)
Bitmovin	2023	Video encoding and delivery technology report	0.8-1.8 (efficient encoding)	N/A
Ericsson	2022	Mobile network video traffic report	0.5-2.0 (mobile average)	N/A

The point is that the shift from broadcasting (multicasting) to unicast entertainment video was destined to dramatically increase internet data consumption.

Will AI Shake Up Advertising as the Internet Did?

Nobody yet knows whether artificial intelligence will mostly reinforce or disrupt advertising markets, much as nobody could have predicted huge shake ups in advance of Google or social media emerging. But it seems entirely possible that AI will assist some channels more than others in gaining or keeping their market share.

That already seems to be happening.

As use of third-party cookies dwindles, another shift in ad placement will follow, further boosting commitments to big retailers who possess actual first-party buyer data. And while internet app and content sites will continue to drive most of the spending, the biggest growth will be advertising on major retailer sites. 

By 2030, forecasters expect major retailers to be scooping up as much as 28 percent of all U.S. advertising spend. 

Channel/Venue	Estimated Spend (USD Billion)	Source
Internet Apps & Content Sites	520	Statista, eMarketer, Interactive Advertising Bureau (IAB)
Major Retailer Sites	290	Statista, eMarketer, Forrester
TV Broadcast Networks	35	Statista, Nielsen
Cable TV Networks	25	Statista, Nielsen
Out-of-Home Advertising	70	Statista, Out of Home Advertising Association of America (OAAA)
Radio & Podcast Advertising	30	Statista, IAB
Print Media	10	Statista, Pew Research Center
Other Channels	45	Industry reports, expert estimates

If one assumes ad revenue since 1995 was earned by different segments including print media, TV broadcasters, cable TV networks, cable TV operators, internet apps and content sites as well as major retailers, the general conclusions would be that print media commitments have shrunk, while revenues earned by the other (mostly electronic) segments have grown. 

Ad volume is dominated now by internet apps and content sites, as well as major retailer channels, which is a big change for advertisers accustomed to using formal media channels. In fact, for some of us, the growth of retailer channels is the biggest surprise. 

Media Channel	Annual Spending (USD Billion)	Percentage Share
Internet Apps & Content Sites	300	52.19%
Major Retailer Sites	120	21.04%
TV Broadcast Networks	49.2	8.55%
Cable TV Networks	32.5	5.65%
Out-of-Home Advertising	40	7.01%
Radio & Podcast Advertising	20	3.48%
Print Media	19.25	3.35%

Advertising at one time underpinned media business models. Today it underpins technology and retailer business models. By 2030, as much as 78 percent of advertising will be going to technology and major retailer firms, while media claims only about 16 percent, including outdoor media such as billboards and displays. 

All that assumes that artificial intelligence will only reinforce existing trends, and not disrupt them. In other words, if AI aids hyper-personalization, predictive targeting, dynamic content optimization or programmatic platforms, it might only reinforce existing trends. 

Likewise, voice interfaces, immersive venues or context awareness should only reinforce existing trends. 

Most observers likely see AI as driving more effectiveness for current methods (targeting, personalization, behavior-based inferences) than creating entirely new venues for placements. 

There is one major caveat. If AI somehow creates new venues, channels or platforms, we could be looking at vastly-different spending patterns in a couple of decades, much as the internet created alternative platforms and venues.