I have been promising to touch on the different aspects of technology related to IMS, and if there is one thing I am good at – it is keeping promises! This time I will start with one of these – compression.
For all you history buffs, let me take you back in time a bit. Once upon a time, there was a great protocol named SIP. It was simple (yeah, sure!) and easy to use. It was text based (Look Mom… you can see messages!), so it was easy to implement, maintain and debug. Many people started to use it and promote it big time. And it did have some great routing and filtering criteria capabilities. So at some point, the 3GPP decided to adopt it for IMS.
So the world was a better place with a nice, simple, text-based protocol, used for signaling purposes over mobile networks. And since it’s signaling, and you don’t have a lot of information you need to convey, it should work. But as time went on, people saw that the messages and the amount of information were actually quite large. When you start adding routing information, authentication and authorization information, billing information and some more – each message becomes REALLY big.
At the end of the day, we had a text-based protocol, with large messages, running over mobile networks. Our problem: mobile networks have lower bandwidths than fixed IP networks (mostly). Also operators out there have to actually pay for the bits you use. For them, more bandwidth required per user for simple calls means less capacity in their cells… and more power consumed by the handset which means a shorter battery life. What to do?
Zip!
You take those messages; you somehow “zip” them and then send them on their way when they take up less space. Since it’s text, it zips quite well.
The secret behind this “zipping” is with a compression protocol called SigComp (RFC 3220, and more) – Signaling Compression.
Everyone agrees: SigComp is nice. It’s general purpose, and it can use different compression algorithms. You can optimize it for the exact messages and scenarios you use. But it’s complex…
By complex I mean that SigComp actually uses bytecode methodology. When you compress messages, you can send along the code that is used to uncompress the messages with the compressed data. This is done using the predefined UDVM (Universal Decompressor Virtual Machine) instructions set (hence bytecode) that outlines the different atomic operations allowed in SigComp.
The process is fairly easy. To compress, you choose an algorithm, use it for your compression, send the compressed data along with the algorithm, and the other side uses the algorithm you sent to decompress.
To make things even more interesting, there’s also a dynamic version of SigComp, which lets you update the SigComp states used in mid-session to provide optimized compression as well.
But then, what could you use as a compression algorithm? Would you go for an LZSS or a Deflate one? Would you do the dynamic optimizations with it? Do you go to patented compressions? Have you thought how much MIPS will this thing take on your mobile???
Lots of questions, huh?
So we have the IMS (3GPP that is). 3GPP means mobile networks. It also means limited bandwidth and the need to compress.
Remember though… there are other standards bodies that do not necessarily need compression, but have adopted IMS architecture. TISPAN and PacketCable, for example, are focused on the wireline and cable telephony networks. So our efforts in this area really are a wider attempt to build a single paradigm for all types of telephony and services! A virtual Utopia! Where everything looks the same.
But our friends who are adopting TISPAN and Packet Cable took a peek at our SigComp in IMS and said, “Sorry. We don’t need it.” Their networks can handle large messages, for them, adding SigComp just adds complexity and requires even more resources.
So, on top of everything else, you are faced with the million dollar compression question: Do you need SigComp or not?
Oh yeah… and what about WiMAX?
So you see, SigComp is only part of the compression story. Next time, we’ll discuss other IMS compression issues.
