Learning to Wirelessly Deliver Consistent and High-Quality Interactive Panoramic Scenes

Wireless interactive panoramic scene delivery imposes unique challenges compared to its wired or non-interactive counterparts. The wireless channel is throughput-constrained, which limits the ability to deliver large and high-quality panoramic images. On the other hand, the interactivity imposes a real-time constraint on the system and limits the use of a playback buffer. Also, wireless inputs are not delivered instantaneously like wired interrupt-based inputs, so the system must predict the user's head pose and the portion of the scene visible to them, called the viewport. This reveals a tradeoff: delivering a portion too small may not cover the viewport if the prediction error is too large, while delivering a portion too large may result in a failed wireless transmission. Likewise, delivering the portion at too high a quality may result in a failed transmission. Despite these challenges, we would like to guarantee an immersive experience for the user by delivering a high-quality and visually consistent panoramic scene. To that end, we aim to maximize the user's quality of experience, which we define as the combination of (1) cumulative quality, (2) long-term consistency, which quantifies the overall variance in perceived quality, and (3) short-term consistency, which quantifies abrupt quality changes. We formulate this problem as a risk-averse multi-armed bandit problem with reward-dependent switching costs, and develop a novel block-based UCB algorithm with opportunistic switching. We derive its theoretical regret upper bound, which matches results in prior work, and corroborate this result in trace-based simulations using a panoramic video streaming data trace.