The congestion control for multimedia applications (Voice over IP, video on demand) is an open issue. We have investigated the congestion control strategies employed by leading multimedia applications such as the WebRTC framework, currently used by Google Hangouts and Skype for VoIP We have found that both applications do not employ an efficient congestion control scheme. We are designing, implementing and experimenting a congestion control algorithm for real-time traffic over the Web.
Nowadays, the Internet is rapidly evolving to become an equally efficient platform for multimedia content delivery. Key examples are YouTube, Skype Audio/Video, IPTV, P2P video distribution platforms such as Coolstreaming or Joost, to name few. While YouTube streams videos using the Transmission Control Protocol (TCP), applications that are time-sensitive such as Video Conferencing employ the UDP because they can tolerate small loss percentages but not delays due to TCP recovery of losses via retransmissions. Since the UDP does not implement congestion control, these applications must implement those functionalities at the application layer. In these papers we experimentally evaluate the Google Congestion Control (GCC) which has been proposed in the RMCAT IETF WG. By setting up a controlled testbed, we have evaluated to what extent GCC flows are able to track the available bandwidth, while minimizing queuing delays, and fairly share the bottleneck with other GCC or TCP flows. We have found that the algorithm works as expected when a GCC flow accesses the bottleneck in isolation, whereas it is not able to provide a fair bandwidth utilization when a GCC flow shares the bottleneck with either a GCC or a TCP flow.
Our experimental investigation has shown that the first version of GCC gets starved when a TCP flow joins the bottleneck (see Fig. below (a)). Moreover, we have found that starvation also occurs when two coexisting GCC flows share a bottleneck (see Fig. below (b) (c)).
To overcome these issues, we have proposed the adaptive threshold mechanism in the last version of the IETF draft which sets the threshold g(i) used by the over-use detector.
Fig. below shows how rate flows dynamics along with one way delay variations are nicely set after the introduction of the adaptive threshold.
This paper investigates Skype Video in order to discover at what extent this application is able to throttle its sending rate to match the unpredictable Internet bandwidth while preserving resource for co-existing best-effort TCP traffic.
Skype is the most popular VoIP application with over 250 million userbase spread all over the world. It is important to study how skype reacts to packet losses in order to infer if a huge amount of skype calls can result in a congestion collapse.
The figure shows the sending rate, the loss rate and the available bandwidth. It can be noticed that Skype adapts its sending rate when the available bandwidth decreases but this adaptation takes 40s, thus leading to high packet loss rates.
For the before mentioned reason Skype is not able to cope with sudden bandwidth variations as it can be seen in the next figure.
Skype's response to bandwidth variation is sluggish and leads to unfriendliness with respect to TCP flows.
The Figure above shows that TCP connection suffers a large number of timeouts.
Two Skype calls have been placed flowing in the same bottleneck in order to investigate if Skype's congestion control is able to guarantee fairness.