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Virtual Reality (VR) and Augmented Reality (AR) technologies have become popular in recent years. Encoding and transmitting the omni-directional or $360^\circ $ video is critical and challenging for those applications. The $360^\circ $ video requires much higher bandwidth than the traditional planar video. A premium quality $360^\circ $ video with 120 frames per second (fps) and 24K resolution can easily consume bandwidth in the range of Gigabits-per-second~\cite1. On the other hand, at any given time, a user only watches a small portion of the $360^\circ$ scope within her Field-of-View (FoV). An effective way to reduce the bandwidth requirement of $360^\circ $ video is through FoV-adaptive streaming, which codes and delivers the predicted FoV region at higher quality, and discards or codes at lower quality the remaining regions. Such strategy has been quite extensively studied for video-on-demand \citefov_adapt_2,fov_adapt_3,1,tile_based_3,qian2016optimizing and live video streaming applications\citelive_1,live_2,live_3, sun2020flocking. Interactive applications, such as conferencing, gaming, and remote collaboration, can also benefit from $360^\circ $ video by creating an immersive environment for participants to interact with each other citeinteractive_gamming \citevr_conferencing \citelee2015outatime. However, realtime coding and streaming of $360^\circ $ video with extremely low latency, required for interactive applications, has not been sufficiently addressed. This work focuses on developing low-latency and FoV-adaptive coding and streaming strategies for interactive $360^\circ$ video streaming. We assume the sender and the receiver are connected by a network path with dynamically varying throughput without short-latency guarantee. The sender is either the video source, or a proxy server relaying the source video. The receiver is either the end user device that directly renders the video, or a local edge server that renders the video and transmit to the end user \citeHou2017.

Streaming of live 360-degree video allows users to follow a live event from any view point and has already been deployed on some commercial platforms. However, the current systems can only stream the video at relatively low-quality because the entire 360-degree video is delivered to the users under limited bandwidth. In this paper, we propose to use the idea of "flocking" to improve the performance of both prediction of field of view (FoV) and caching on the edge servers for live 360-degree video streaming. By assigning variable playback latencies to all the users in a streaming session, a "streaming flock" is formed and led by low latency users in the front of the flock. We propose a collaborative FoV prediction scheme where the actual FoV information of users in the front of the flock are utilized to predict of users behind them. We further propose a network condition aware flocking strategy to reduce the video freeze and increase the chance for collaborative FoV prediction on all users. Flocking also facilitates caching as video tiles downloaded by the front users can be cached by an edge server to serve the users at the back of the flock, thereby reducing the traffic in the core network. We propose a latency-FoV based caching strategy and investigate the potential gain of applying transcoding on the edge server. We conduct experiments using real-world user FoV traces and WiGig network bandwidth traces to evaluate the gains of the proposed strategies over benchmarks. Our experimental results demonstrate that the proposed streaming system can roughly double the effective video rate, which is the video rate inside a user's actual FoV, compared to the prediction only based on the user's own past FoV trajectory, while reducing video freeze. Furthermore, edge caching can reduce the traffic in the core network by about 80%, which can be increased to 90% with transcoding on edge server.