Abstract
Purpose – This paper aims to argue on the efficiency of Quality of Service (QoS)-based adaptive streamingwith regards to perceived quality Quality of Experience (QoE). Although QoS parameters are extensivelyused even by high-end adaptive streaming algorithms, achieved QoE fails to justify their use in real-timestreaming videos with high motion. While subjective measurements of video quality are difficult to beapplied at runtime, objective QoE assessment can be easier to automate. For end-to-end QoS optimization oflive streaming of high-motion video, objective QoE is a more applicable approach. This paper contributes tothe understanding of how specific QoS parameters affect objective QoE measurements on real-timehigh-motion video streaming.Design/methodology/approach – The paper approached the question through real-life andextensive experimentation using the Skype adaptive mechanisms. Two Skype terminals wereconnected through a QoS impairment box. A reference video was used as input to one Skype terminaland streamed on one direction. The impairment box was stressing the stream with different conditions.Received video was stored and compared against the reference video.Findings – After the experimental analysis, the paper concludes that adaptive mechanisms based onQoS-related heuristics fail to follow unexpected changes to stream requirements. High-motion videosare an example of this variability, which makes the perceived quality sensitive to jitter more than topacket loss. More specifically, Skype seems to use if-else heuristics to decide its behavior to QoSchanges. The weaknesses to high-motion videos seem to lie on this rigidity.Research limitations/implications – Due to the testbed developed, the results may be different ifexperiments are run over networks with simultaneous streams and a variety of other traffic patterns. Finally,other streaming clients and algorithms would contribute to a more reliable generalization.Practical implications – The paper motivates video streaming engineers to emphasize their effortstoward QoE and end-to-end optimization.Originality/value – The paper identifies the need of a generic adaptive streaming algorithm able toaccommodate a big range of video characteristics. The effect of QoS variability to high-motion videostreaming helps in modeling and design.