HiVAT: Improving QoE for Hybrid Video Streaming Service With Adaptive Transcoding

Yuanwei Zhu, Yakun Huang, Xiuquan Qiao, Jian Tang, Xiang Su

Abstract: Mobile video streaming enables flexible delivery of videos to mobile devices, supporting emerging video formats. The transition from conventional 2D videos to immersive formats, such as virtual reality and holographic videos, significantly increases the demand for computation and network resources. Existing streaming techniques are predominantly developed for specific video types, neglecting fair adaptive transmission and optimal resource utilization in services involving multiple video types. This paper investigates hybrid video streaming, encompassing 2D, 360-degree, and volumetric videos. To accommodate resource-intensive hybrid video streaming on mobile devices, we propose HiVAT, an adaptive transcoding-based system that ensures Quality of Experience (QoE) for each stream type. We contribute 1) a transcoding-based framework to address the challenges of high bandwidth and decoding overhead on mobile devices; 2) a universal QoE model involving traditional factors, viewport smoothness, degree of immersion, etc., for transcoded video streams; 3) a multi-agent adaptive bitrate controller that collaboratively determines hybrid video quality levels to achieve high and fair QoE across multiple streams; and 4) a learning-based task scheduler to optimize computation resource usage, thereby improving the overall serviceability of the system. We evaluate HiVAT against state-of-the-art methods, witnessing an average QoE improvement of 5.9% and 9.9% on linear and logarithmic metrics, respectively.