An Online Learning Based Path Selection for Multipath Video Telephony Service in Overlay
Songyang Zhang

TL;DR
This paper proposes an online learning-based path selection method using multi-armed bandits and a congestion control algorithm for overlay networks to enhance multipath video telephony quality in wireless environments.
Contribution
It introduces a novel path selection approach combining multi-armed bandits with a congestion control algorithm tailored for overlay networks in video telephony.
Findings
Improved throughput and video quality demonstrated in experiments.
Effective congestion avoidance maintaining fairness and stability.
Enhanced bandwidth utilization through adaptive path selection.
Abstract
Even real time video telephony services have been pervasively applied, providing satisfactory quality of experience to users is still a challenge task especially in wireless networks. Multipath transmission is a promising solution to improve video quality by aggregating bandwidth. In existing multipath transmission solutions, sender concurrently splits traffic on default routing paths and has no flexibility to select paths. When default paths fall into severe congestion and the available bandwidth decreases, sender has to decrease video quality by reducing resolution or encoding bitrate. Deploying relay servers in current infrastructure to form overlay network provides path diversity. An online learning approach based on multi-armed bandits is applied for path selection to harvest maximum profit. Further, a congestion control algorithm adapted from BBR is implemented to probe bandwidth…
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Taxonomy
TopicsAdvanced Wireless Network Optimization · Network Traffic and Congestion Control · Wireless Networks and Protocols
