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The highly directional nature of the millimeter wave (mmWave) beams pose several challenges in using that spectrum for meeting the communication needs of immersive applications. In particular, the mmWave beams are susceptible to misalignments and blockages caused by user movements. As a result, mmWave channels are vulnerable to large fluctuations in quality, which in turn, cause disproportionate degradation in end-to-end performance of Transmission Control Protocol (TCP) based applications. In this paper, we propose a reinforcement learning (RL) integrated transport-layer plugin, Millimeter wave based Immersive Agent (MIA), for immersive content delivery over the mmWave link. MIA uses the RL model to predict mmWave link bandwidth based on the real-time measurement. Then, MIA cooperates with TCP’s congestion control scheme to adapt the sending rate in accordance with the predictions of the mmWave bandwidth. To evaluate the effectiveness of the proposed MIA, we conduct experiments using a mmWave augmented immersive testbed and network simulations. The evaluation results show that MIA improves end-to-end immersive performance significantly on both throughput and latency. 

Millimeter wave (mmWave) access networks have the potential to meet the high-throughput and low-latency needs of immersive applications. However, due to the highly directional nature of the mmWave beams and their susceptibility to beam misalignment and blockage resulting from user movements and rotations, the associated mmWave links are vulnerable to large channel fluctuations. These fluctuations result in disproportion- ately adverse effects on performance of transport layer protocols such as Transmission Control Protocol (TCP). To overcome this challenge, we propose a network layer solution, COded Taking And Giving (COTAG) scheme to sustain low-latency and high- throughput end-to-end TCP performance in dually connected networks. In particular, COTAG creates network encoded packets at the network gateway and each access point (AP) aiming to adaptively take the spare bandwidth on each link for transmis- sion. Further, if one link bandwidth drops due to user movements, COTAG actively abandons the transmission opportunity by conditionally dropping packets. Consequently, COTAG actively adapts to link quality changes in mmWave access network and enhances the TCP performance without jeopardizing the latency of immersive content delivery. To evaluate the effectiveness of the proposed COTAG, we conduct experiments using off-the- shelf APs and network simulations. The evaluation results show that COTAG improves end-to-end TCP performance significantly on both throughput and latency. 

This paper presents the results of motion-tracking synchronized millimeter wave (mmWave) link bandwidth fluctuations while a user is engaged in immersive augmented/virtual reality applications. Our system, called MITRAS, supports ex- tensive exploration of human-induced impacts on mmWave link bandwidth during immersive experience. MITRAS adopts the packet train measurement application to track link bandwidth fluctuations. Meanwhile, the user movements are tracked using an Oculus Quest 2 headset. Through investigating the impacts of human movements on link bandwidth fluctuations, we further propose a link state prediction model to shed light on higher layer protocol design for immersive applications over mmWave links.