Engineers Struggle to Move Mobile Video Without Ruining It

Intel, Cisco and Verizon are pouring $3.3 million into research at five prominent universities to improve video delivery over wireless networks. The first goal of the Video Aware Wireless Networks (VAWN) program? Find a good way to measure mobile video quality.

Understanding Video Quality

As consumers watch ever increasing amounts of video on their mobile devices, network congestion threatens to ruin the experience for mobile users - and give carriers and equipment makers fits trying to accommodate all the traffic. To figure out the best ways to address the problem while still delivering a great picture, though, you need to know - in great detail - what a great picture actually is.  Therefore, VAWN's initial aim is to assess subjective video quality in quantitative terms, according to execs from the three companies who spoke at a roundtable in San Francisco this week.

It turns out that question is a lot more complicated than it sounds, and includes as many perceptual issues as technical concerns. Because viewers perceive quality differently depending on what they're watching - sports versus talking heads, for example - quality isn't about throughput but experience, explained Jeff Foerster, principal engineer and wireless researcher at Intel Labs. That's why VAWN researchers partnered with psychology departments to better understand how the brain comprehends different kinds of video on various devices.

The research is important to finding ways to deliver the best video experience to the most people when the networks get overloaded; that is, minimize the problems that annoy people most. That could mean adjusting the streams' algorithms so the network knows how to deal with particular kinds of content and devices, and understanding the impact of data compression, caching, and storage on video quality. One key is better cooperation between different parts of the network. Video, like other network traffic, is made up of packets of data, but “not all packets need to be treated the same,” Foerster said. “Some packets are more important than others to maximizing perceived video quality.”

Chris Neisinger, Verizon’s executive director of network planning, explained that “right now, we take video in forms that have been created for wired delivery, without care for what the [available network] bandwidth is.” The goal is to figure out “what parts of the video can I change, so that the cognitive perception is still high quality? How can we create a version of the video that’s really highly suited for delivery over wireless?” 

Neisinger said cellular networks already do this on voice calls, making smart tradeoffs to make sure maximum number of users have the best experience. “We don’t have that in the video world.”

To make things more complicated, the measurements must vary by device: “You can’t just take the subjective score for a TV and directly apply it to mobile devices,” Foerster said.

The Bigger Picture

Measuring subjective video quality is only the first step toward the larger goal of delivering high-quality video while boosting network capacity to handle the ever-increasing flood of data. The issue can’t be ignored: In 5 years an estimated 90% of Net traffic will be video, and 66% of mobile traffic will be video. Video traffic is expected to grow 66 times based on the Cisco Visual Networking Index (VNI), but carriers simply can't afford to spend 66 times the cost to boost network capacity. “When you look at a number like 66X, we have to find more efficient ways to do that,” Verizon’s Neisinger said.

Different kinds of video have different requirements and there’s no intrinsic need to treat them all the same, Foerster pointed out. For example, streaming video can use a long buffer - several seconds, say - but that doesn’t work in videoconferencing. Similarly, fast-action sports video needs a higher bit rate, but you may be able to get away with a much lower bit rate for relatively static talking-head newscasts.

Some of VAWN's approaches to solving these problems have to do with the video characteristics - how the stream is compressed, for example. But others are all about network management. “Instead of letting [the streams] all fight to get best possible bandwidth - which is basically what they do today… We need to manage that to increase the satisfaction of the largest number of users instead of some who get really high quality while others get lower quality.” 

In addition to the technological and perceptual aspects, these discussions also have a political element. “It starts getting into the discussion of Net Neutrality,” said Flavio Bonomi, Cisco Fellow and Vice President and Head of the Advanced Architecture and Research Organization at Cisco Systems. “When you treat different content in different ways… It’s a very difficult discussion, but it comes up when you allocated different bandwidth to different streams that might have different importance for users.”

A New Model For Research

The program's third goal is to improve the way companies work with universities. “With the pace of change in the network, we need to provide input to the universities so their research can keep up with the pace of change,” said Verizon’s Neisinger. Basically, to tell them, “here’s how we would use that technology.”

The companies sent out a request for proposals to major universities in 2009, and out of 25 responses chose to work with five: the University of Texas at Austin, Cornell, University of California San Diego, University of Southern California and Moscow State University. The universities devote three to four faculty members to the project, each with four to five graduate students. All results are posted on a public website and the research is not patented. Professors are encouraged to work with each other, and instead of just tossing the stuff over the fence and then forgetting about it, the team holds regular reviews and sharing sessions. “We’re very results-oriented,” Foerster said.

The project is now in year two of its three-year, $1.1 million-per-year plan. By then end of the program, the VAWN team hopes to have a good method to measure perceived video quality, as well as algorithms and software tools to use that measurement to make network decisions.