A Joint Bitrate and Buffer Control Scheme for Low-Latency Live Streaming

Chen, Si; Yuan, Zhang; Peng, Huan Xin; Yan, Jinyao

doi:10.1007/978-3-030-36204-1_31

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, many recent papers build artificial neural networks so that they could find the optimal bitrate adaptation scheme. In [46], the authors develop an algorithm for joint bitrate and buffer control for low-latency video streaming. In the proposed scheme, the algorithm dynamically adjusts both the bitrate of the downloaded video and the playback rate to reduce the probability of video stalling.…”

Section: A Video Quality Adaptationmentioning

confidence: 99%

Adaptive Cloud-Based Extended Reality: Modeling and Optimization

et al. 2021

View full text Add to dashboard Cite

Extended Reality (XR)-which includes Virtual Reality and Augmented Reality-promises to bring the virtual and telepresence experience to another level. Unfortunately, solutions leveraging these technologies require special high-performance computing platforms that degrade the cost-benefit balance. Moving processing to the cloud solves this problem but imposes strict requirements on data transmission reliability, bandwidth, and delays. The satisfaction of these requirements becomes an extremely challenging problem in the presence of other types of delay-sensitive traffic, such as remote control, industrial automation, or the control commands of the Cloud XR application itself. This article studies the joint service of the adaptive Cloud XR traffic with other high-priority delay-sensitive traffics. The paper develops an analytical model of the considered communication system. The model represents the system as a discrete state Markov chain and estimates the quality of experience for Cloud XR users in various scenarios. Using the model, the paper estimates the network capacity for the Cloud XR traffic and optimizes the bitrate adaptation function of the Cloud XR video streaming application. The goal of the optimization is to improve the visual quality of the virtual environment observed by the users, subject to the constrained probability of image impairments due to excessive delivery delays. Numerical results demonstrate the high accuracy of the developed model and the benefits provided by the optimization. INDEX TERMS Cloud XR, heterogeneous traffic, high-priority traffic, real-time adaptive video, virtual reality, quality of experience, queueing systems, analytical models, Markov chain.

show abstract

Section: A Video Quality Adaptationmentioning

confidence: 99%

Adaptive Cloud-Based Extended Reality: Modeling and Optimization

et al. 2021

View full text Add to dashboard Cite

show abstract

EVeREst: Bitrate Adaptation for Cloud VR

2021

View full text Add to dashboard Cite

Cloud Virtual Reality (VR) technology is expected to promote VR by providing a higher Quality of Experience (QoE) and energy efficiency at lower prices for the consumer. In cloud VR, the virtual environment is rendered on the remote server and transmitted to the headset as a video stream. To guarantee real-time experience, networks need to transfer huge amounts of data with much stricter delays than imposed by the state-of-the-art live video streaming applications. To reduce the burden imposed on the networks, cloud VR applications shall adequately react to the changing network conditions, including the wireless channel fluctuations and highly variable user activity. For that, they need to adjust the quality of the video stream adaptively. This paper studies video quality adaptation for cloud VR and improves the QoE for cloud VR users. It develops a distributed, i.e., with no assistance from the network, bitrate adaptation algorithm for cloud VR, called the Enhanced VR bitrate Estimator (EVeREst). The algorithm aims to optimize the average bitrate of cloud VR video flows subject to video frame delay and loss constraints. For that, the algorithm estimates both the current network load and the delay experienced by separate frames. It anticipates the changes in the users’ activity and limits the bitrate accordingly, which helps prevent excess interruptions of the playback. With simulations, the paper shows that the developed algorithm significantly improves the QoE for the end-users compared to the state-of-the-art adaptation algorithms developed for MPEG DASH live streaming, e.g., BOLA. Unlike these algorithms, the developed algorithm satisfies the frame loss requirements of multiple VR sessions and increases the network goodput by up to 10 times.

show abstract

A Joint Bitrate and Buffer Control Scheme for Low-Latency Live Streaming

Cited by 2 publications

References 15 publications

Adaptive Cloud-Based Extended Reality: Modeling and Optimization

Adaptive Cloud-Based Extended Reality: Modeling and Optimization

EVeREst: Bitrate Adaptation for Cloud VR

Contact Info

Product

Resources

About