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Even real time video telephony services have been pervasively applied,providing satisfactory quality of experience to users is still a challenge taskespecially in wireless networks. Multipath transmission is a promising solutionto improve video quality by aggregating bandwidth. In existing multipathtransmission solutions, sender concurrently splits traffic on default routingpaths and has no flexibility to select paths. When default paths fall intosevere congestion and the available bandwidth decreases, sender has to decreasevideo quality by reducing resolution or encoding bitrate. Deploying relayservers in current infrastructure to form overlay network provides pathdiversity. An online learning approach based on multi-armed bandits is appliedfor path selection to harvest maximum profit. Further, a congestion controlalgorithm adapted from BBR is implemented to probe bandwidth and to avoid linkcongestion. To maintain throughput stability and fairness, a smaller probe upgain value is used and the cycle length in bandwidth probe phase is randomized.To reduce delay, the inflight packets will be reduced to match with theestimated bandwidth delay product in the probe down phase. Experiments areconducted to verify the effectiveness the proposed solution to improvethroughput and quality in video communication service.

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