Case for 5G-aware video streaming applications [conference paper]

Conference

Workshop on 5G Measurements, Modeling, and Use Cases - August 23, 2021

Authors

Eman Ramadan (Ph.D. student), Arvind Narayanan (Ph.D. student), Udhaya Kumar Dayalan (Ph.D. student), Rostand AK Fezeu (Ph.D. student), Feng Qian (associate professor), Zhi-Li Zhang (professor)

Abstract

Recent measurement studies show that commercial mmWave 5G can indeed offer ultra-high bandwidth (up to 2 Gbps), capable of supporting bandwidth-intensive applications such as ultra-HD (UHD) 4K/8K and volumetric video streaming on mobile devices. However, mmWave 5G also exhibits highly variable throughput performance and incurs frequent handoffs (e.g., between 5G and 4G), due to its directional nature, signal blockage and other environmental factors, especially when the device is mobile. All these issues make it difficult for applications to achieve high Quality of Experience (QoE). In this paper, we advance several new mechanisms to tackle the challenges facing UHD video streaming applications over 5G networks, thereby making them {\em 5G-aware}. We argue for the need to employ machine learning (ML) for effective throughput prediction to aid applications in intelligent bitrate adaptation. Furthermore, we advocate {\em adaptive content bursting}, and {\em dynamic radio (band) switching} to allow the 5G radio network to fully utilize the available radio resources under good channel/beam conditions, whereas dynamically switched radio channels/bands (e.g., from 5G high-band to low-band, or 5G to 4G) to maintain session connectivity and ensure a minimal bitrate. We conduct initial evaluation using real-world 5G throughput measurement traces. Our results show these mechanisms can help minimize, if not completely eliminate, video stalls, despite wildly varying 5G throughput.

Link to full paper

Case for 5G-aware video streaming applications

Keywords

mobile networking, 5G