  High quality (HQ) video services occupy large portions of the total bandwidth
and are among the main causes of congestion at network bottlenecks. Since video
is resilient to data loss, throwing away less important video packets can ease
network congestion with minimal damage to video quality and free up bandwidth
for other data flows. Frame type is one of the features that can be used to
determine the importance of video packets, but this information is stored in
the packet payload. Due to limited processing power of devices in high
throughput/speed networks, data encryption and user credibility issues, it is
costly for the network to find the frame type of each packet. Therefore, a fast
and reliable standalone method to recognize video packet types at network level
is desired. This paper proposes a method to model the structure of live video
streams in a network node which results in determining the frame type of each
packet. It enables the network nodes to mark and if need be to discard less
important video packets ahead of congestion, and therefore preserve video
quality and free up bandwidth for more important packet types. The method does
not need to read the IP layer payload and uses only the packet header data for
decisions. Experimental results indicate while dropping packets under packet
type prediction degrades video quality with respect to its true type by 0.5-3
dB, it has 7-20 dB improvement over when packets are dropped randomly.

Image and Video Processing