TY - GEN
T1 - QoE estimation-based server benchmarking for virtual video delivery platform
AU - Koskimies, Lauri
AU - Taleb, Tarik
AU - Bagaa, Miloud
N1 - | openaire: EC/H2020/723172/EU//5GPagoda
PY - 2017/5
Y1 - 2017/5
N2 - This paper introduces a Quality of Experience (QoE) estimation-based server benchmarking system, which can be utilized as a part of QoE-optimized resource provisioning in our envisioned virtual video delivery platform. The system has been targeted for benchmarking virtual video streaming servers, i.e., virtual server flavors deployed in a cloud environment, based on resulting QoE estimates. The paper also presents another layer to the benchmarking by showing how to optimize stream segment duration in terms of estimated QoE. The QoE estimation in the system is based on a Pseudo-Subjective Quality Assessment (PSQA) method developed for video streaming. Output of the system, i.e., QoE estimation-based benchmarks, helps to find out how different factors can affect video streaming QoE which in turn makes parameter and resource optimizations possible. Moreover, the paper presents experimental benchmarking results obtained in a cloud environment.
AB - This paper introduces a Quality of Experience (QoE) estimation-based server benchmarking system, which can be utilized as a part of QoE-optimized resource provisioning in our envisioned virtual video delivery platform. The system has been targeted for benchmarking virtual video streaming servers, i.e., virtual server flavors deployed in a cloud environment, based on resulting QoE estimates. The paper also presents another layer to the benchmarking by showing how to optimize stream segment duration in terms of estimated QoE. The QoE estimation in the system is based on a Pseudo-Subjective Quality Assessment (PSQA) method developed for video streaming. Output of the system, i.e., QoE estimation-based benchmarks, helps to find out how different factors can affect video streaming QoE which in turn makes parameter and resource optimizations possible. Moreover, the paper presents experimental benchmarking results obtained in a cloud environment.
U2 - 10.1109/ICC.2017.7996445
DO - 10.1109/ICC.2017.7996445
M3 - Conference contribution
SN - 978-1-4673-9000-2
T3 - IEEE International Conference on Communications (ICC)
BT - 2017 IEEE International Conference on Communications (ICC)
PB - IEEE
T2 - IEEE International Conference on Communications
Y2 - 21 May 2017 through 25 May 2017
ER -