Abstract
Multimedia applications such as video streaming services have become popular, especially with the rapid growth of users, devices, increased availability and diversity of these services over the internet. In this case, service providers and network administrators have difficulties ensuring end-user satisfaction because the traffic generated by such services is more exposed to multiple network quality of service impairments, including bandwidth, delay, jitter, and loss ratio. This paper proposes an intelligent-based multimedia traffic routing framework that exploits the integration of a reinforcement learning technique with software-defined networking to explore, learn and find potential routes for video streaming traffic. Simulation results through a realistic network and under various traffic loads, demonstrate the proposed scheme’s effectiveness in providing improved end-user viewing quality, higher throughput and lower video quality switches when compared to the existing techniques.
Original language | English |
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Number of pages | 7 |
Publication status | Accepted/In press - 3 Nov 2022 |
Event | The 15th International Conference on the Developments in eSystems Engineering (DeSE2022) - The American University in Baghdad, Baghdad, Iraq Duration: 9 Jan 2023 → 12 Jan 2023 https://dese.org.uk/dese-2022/ |
Conference
Conference | The 15th International Conference on the Developments in eSystems Engineering (DeSE2022) |
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Abbreviated title | DeSE2022 |
Country/Territory | Iraq |
City | Baghdad |
Period | 9/01/23 → 12/01/23 |
Internet address |
Keywords
- multimedia traffic
- QoE
- QoS
- SDN
- reinforcement learning