The Upstream Matters: Impact of Uplink Performance on YouTube 360° Live Video Streaming in LTE

Abstract

Live video streaming services are gaining momentum as network and terminal capabilities improve. However, 360º live video streaming services pose new challenges due to its high bandwidth and computational requirements both on the user and service provider. In this paper, a study of the impact of the uplink of a cellular network on the performance of 360º live video streaming in YouTube is presented. Unlike previous works, the analysis focuses on the upstream between the video source and the server, not on the downstream between the server and viewers. To this end, a measurement campaign is conducted where a live video feed is transmitted and received through YouTube 360º platform in a pilot Long Term Evolution (LTE) system. During the tests, a large dataset of real traces is collected at different protocol layers, both in upstream and downstream, to check the correlation between TCP/IP metrics and key service performance indicators (e.g., video segment quality and end-to-end latency). Results show that uplink performance has a strong impact on the latency perceived by the user, which is critical for the considered live services.INDEX TERMS YouTube, live video streaming, 360-degree video, latency, Quality of Experience, uplink.

DLLBVS: Design of a High-Efficiency Deep Learning based Low-BER Video Streaming Model for High-Noise Wireless Networks

“…Models that use Deep Reinforcement Learning (DRL) [14], Linear Optimizations [15], Online Bitrate Selection [16], Deep Learning Optimizations [17], cycle vector-quantized variational autoencoder (cycle-VQ-VAE) [18], Flexible Latency Aware Streaming (FLAS) [19], and Reinforcement Learning-Based Rate Adaptation (RLRA) [20], for dynamic control over streaming operations are discussed & evaluated under different scenarios. These models are further extended via the work in [21,22,23,24,25], which propose use of Shift-Tile-Tracking (STC), LSTM based streaming, scalable-high-efficiency-video-coding (SHVC) with device-to-device communications, Sliding-Window Forward Error Correction (SW FEC), and context-aware streaming, which enables real-time processing for different video types.…”

“…[10] on a log-rectilinear transformation for foveated 360-degree video streaming and the research by Jiménez et al [17] [34,41,42,43] in their respective studies on QoE optimization in DASH-based multiview video streaming and improving QoE for low-latency live video streaming. Additionally, Chakareski et al [32,44,45] provide insights into end-to-end optimization for viewport-driven 360° video streaming, emphasizing the importance of user navigation modeling and rate-distortion analysis.…”

Machine-Learning-Based Uplink Throughput Prediction from Physical Layer Measurements

“…Due to this asymmetric deployment, the DL channel has a higher transmission capacity than the UL. However, the need for UL data has increased considerably with the rapid increase in the use of applications that require UL throughput, such as AV control, machine type communication devices (MTDs) smart body area networks (SmartBAN), IoT, wireless sensor networks, video conferencing, file sharing, VoIP, surveillance cameras, peer-to-peer (P2P) and cloud services [8][9][10][11][12][13][14]. Although many studies have focused on DL channel prediction due to the much higher demand for DL data rate before, UL traffic estimation has gained significance since the channel asymmetry in favor of DL throughput is closing rapidly [15].…”