NEWCAST: Anticipating resource management and QoE provisioning for mobile video streaming

Abstract

Abstract-The knowledge of future throughput variations in mobile networks becomes more and more possible today thanks to the rich contextual information provided by mobile applications and services and smartphone sensors. It is even likely that such contextual information, which may include traffic, mobility and radio conditions will lead to a novel agile resource management not yet thought of. In this paper, we propose an framework (called NEWCAST) that anticipates the throughput variations to deliver video streaming content. We develop an optimization problem that realizes a fundamental trade-off among critical metrics that impact the user's perceptual quality of experience (QoE) and the cost of system utilization. Both simulated and real-world throughput traces collected from [1] were carried out to evaluate the performance of NEWCAST. In particular, we show from our numerical results that NEWCAST provides the efficiency that the new 5G architectures require in terms of computational complexity and robustness. We also implement a prototype system of NEWCAST and evaluate it in a real environment with a real player to show its efficiency and scalability compared to baseline adaptive bitrate algorithms.Index Terms-Adaptive video streaming, quality of experience, resource allocation, mobile network, throughput prediction.

Learning from experience: A dynamic closed-loop QoE optimization for video adaptation and delivery

“…iii) For each strategy, compute the QoE metrics then apply the vector of weights W to find the best solution. To find all the possible ascending strategies, use the tree of choice described in [14]IV-3.…”

“…The main shortcoming of their approach is that it is only suited for classical video streaming as it ignores important visual quality metrics related to adaptive streaming such as video resolution and bitrate distribution. Holding the same assumption, authors in [14] proposed a proactive video content delivery algorithm, called NEWCAST, to adjust the quality of adaptive video streaming over the future horizon. Their work was afterward extended in [15] to make their algorithm well suited for unperfected throughput prediction.…”

“…• We exploit the knowledge of future throughput variations in order to solve the optimization problem addressed in [16] in a smoother and faster manner based on similar mathematical analysis than in [14]. • We design a closed-loop framework based on clientserver interactions to learn the overall users' perceptions and to fittingly optimize the quality of the streaming.…”

Forecast scheduling for mobile users

“…The idea of improving resource allocation by anticipating the future state of the mobile user has recently became an active research arena, mainly for video streaming services, see for example [10] and [11]. This research area has been denoted as anticipatory or proactive resource allocation.…”

Anticipating Resource Management and QoE for Mobile Video Streaming under Imperfect Prediction

“…In [11], authors present a threshold-based transmission schedule which optimizes network utilization while guaranteeing users' QoE for simple classical video streaming. This framework was generalized in [1] to strike a balance between network utilization and users' QoE for adaptive video streaming. In particular, [1] proposes a proactive video content delivery algorithm, called NEWCAST, that adjusts the video quality over a long future horizon, assuming a perfect throughput prediction.…”