Performance analysis of adaptive streaming algorithms for a low-latency environment

Sammanfattning: Streaming video over the internet can face issues when met with poor and varying network conditions, which can be especially noticeable when streaming live video such as security footage or video calling. To handle this, there exists congestion control algorithms that monitor network conditions based on feedback from the receiver and adapt the video output. This thesis aims to implement and compare three different algorithms NADA, SCReAM and GCC as part of Axis' video streaming system and IoT camera, and to perform a performative analysis in both environments. To perform the analysis, a set of realistic situations are simulated inside the same framework that is used in production, as well as implementing and testing out a Proof on Concept directly on camera hardware. The algorithms are implemented into Axis own WebRTC system, and run inside a container system with networking tools to monitor performance. A similar networking performance test is run directly on camera hardware to test out how it behaves on real hardware. This thesis concludes that the NADA algorithm is an ideal choice when there is not a lot of latency present, having great utilization of the available link. However, in the presence of non-constant network delay it, together with SCReAM fails to utilize the link and only GCC can maintain a proper sending rate; GCC is shown to be a very good general-purpose algorithm. SCReAM shows a constant lower utilization of the available link, matching its target use of mobile networking and with the best measure of round-trip time in low-latency tests it is a good fit for remote-controlled devices.