TY - GEN
T1 - MATURE
T2 - 34th Workshop on Network and Operating System Support for Digital Audio and Video, NOSSDAV 2024
AU - Nolan, Killian
AU - Raca, Darijo
AU - Provan, Gregory
AU - Zahran, Ahmed
N1 - Publisher Copyright:
© 2024 Owner/Author(s).
PY - 2024/4/15
Y1 - 2024/4/15
N2 - Accurate Throughput Prediction (TP) represents a cornerstone for reliable adaptive streaming in challenging mediums, such as cellular networks. Challenged by the highly dynamic wireless medium, recent state-of-The-Art solutions adopt Deep Learning (DL) models to improve TP accuracy. However, these models perform poorly in critical, rare network conditions, leading to degraded user Quality of Experience (QoE). Such performance results from depending solely on the model's capacity and power of learning, without integrating system knowledge into the design. In this paper, we propose MATURE, a novel multi-stage DL-based TP model designed to capture network operating context to improve prediction accuracy and user experience. MATURE's operation involves characterising the operating context before estimating the network throughput. Our performance evaluation shows that MATURE improves the average user QoE by 4%-90% in critical network conditions when compared to state-of-The-Art.
AB - Accurate Throughput Prediction (TP) represents a cornerstone for reliable adaptive streaming in challenging mediums, such as cellular networks. Challenged by the highly dynamic wireless medium, recent state-of-The-Art solutions adopt Deep Learning (DL) models to improve TP accuracy. However, these models perform poorly in critical, rare network conditions, leading to degraded user Quality of Experience (QoE). Such performance results from depending solely on the model's capacity and power of learning, without integrating system knowledge into the design. In this paper, we propose MATURE, a novel multi-stage DL-based TP model designed to capture network operating context to improve prediction accuracy and user experience. MATURE's operation involves characterising the operating context before estimating the network throughput. Our performance evaluation shows that MATURE improves the average user QoE by 4%-90% in critical network conditions when compared to state-of-The-Art.
KW - deep learning
KW - mobile networks
KW - throughput prediction
KW - video streaming
UR - https://www.scopus.com/pages/publications/85192021785
U2 - 10.1145/3651863.3651878
DO - 10.1145/3651863.3651878
M3 - Conference proceeding
AN - SCOPUS:85192021785
T3 - NOSSDAV 2024 - Proceedings of the 2023 34th Workshop on Network and Operating System Support for Digital Audio and Video
SP - 15
EP - 21
BT - NOSSDAV 2024 - Proceedings of the 2023 34th Workshop on Network and Operating System Support for Digital Audio and Video
PB - Association for Computing Machinery, Inc
Y2 - 15 April 2024 through 18 April 2024
ER -