Gene

Dynamic bandwidth allocation

Engineering

Provisioning

Bandwidth (computing)

Instruction prefetch

Robustness (evolution)

Real-time computing

Degree (music)

Biochemistry

Computer science

Acoustics

Computer network

Physics

Video on demand

Cache

Operations management

Video quality

Artificial intelligence

Field of view

Chemistry

Metric (unit)

IEEE Journal on Emerging and Selected Topics in Circuits and Systems

360° video on-demand streaming is a key component of the emerging virtual reality and augmented reality applications. In such applications, sending the entire 360° video demands extremely high network bandwidth that may not be affordable by today's networks. On the other hand, sending only the predicted user's field of view (FoV) is not viable as it is hard to achieve perfect FoV prediction in on-demand streaming, where it is better to prefetch the video multiple seconds ahead, to absorb the network bandwidth fluctuation. This paper proposes a twotier solution, where the base tier delivers the entire 360° span at a lower quality with a long prefetching buffer, and the enhancement tier delivers the predicted FoV at a higher quality using a short buffer. The base tier provides robustness to both network bandwidth variations and FoV prediction errors. The enhancement tier improves the video quality if it is delivered in time and FoV prediction is accurate. We study the optimal rate allocation between the two tiers and buffer provisioning for the enhancement tier to achieve the optimal trade-off between video quality and streaming robustness. We also design periodic and adaptive optimization frameworks to adapt to the bandwidth variations and FoV prediction errors in realtime. Through simulations driven by real LTE and WiGig network bandwidth traces and user FoV traces, we demonstrate that the proposed two-tier systems can achieve a high-level of quality-of-experience in the face of network bandwidth and user FoV dynamics.

A Two-Tier System for On-Demand Streaming of 360 Degree Video Over Dynamic Networks