A hybrid model of the Akamai adaptive streaming control system

Cicco, Luca De; Cofano, Giuseppe; Mascolo, Saverio

doi:10.1016/j.nahs.2015.12.007

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…De Cicco et al [17] formalizes the behavior of an Akamai video streaming session. The system is modeled as a hybrid automaton, using upon others the video level, the current rate, and the playout buffer as state variables.…”

Section: Models For Has Behaviormentioning

confidence: 99%

Computing QoE-Relevant Adaptive Video Streaming Metrics Using Discrete-Time Analysis

Schwarzmann

Breitbach

Zinner

2019

2019 22nd Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN)

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HTTP Adaptive Streaming (HAS) is the de-facto standard for video delivery over the Internet. Splitting the video clip into small segments and providing multiple quality levels per segment allows the client to dynamically adapt the quality to current network conditions. The performance of HAS, and as a consequence the user Quality of Experience (QoE), is influenced by a multitude of parameters. This includes adjustable settings like quality switching thresholds, the initial buffer level, or the maximum buffer, as well as video characteristics like segment duration or the variation of segment sizes along the video. Finding an appropriate tuning of those parameters still remains a challenge, which is mainly tackled by performing testbed measurements or simulative analysis. Due to the large problem space and the complex interactions of the involved influence factors, a holistic comparison of a multitude of parameter settings is extremely time intensive. To tackle this problem, we propose to enhance a GI/GI/1 system with pq-policy, which models video buffer behavior, with the capability to switch between different quality levels. This allows to investigate all relevant QoE influence factors for HAS-based video delivery. In a first evaluation, we illustrate the impact of different quality switching thresholds on the QoE influence factors for varying network conditions.

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Section: Models For Has Behaviormentioning

confidence: 99%