Joint Adaptive Rate and Scheduling for Video Streaming in Multi-Cell Cellular Wireless Networks

Abstract

We consider adaptive-rate scheduling for downlink unicast transmissions of video streams over cellular wireless networks. We study a service under which each mobile client receives requested video streams at a variable Quality of Experience (QoE) level, based on its experienced communication quality condition. We employ a proxy-video manager at the base station node. The manager classifies users into two groups, based on their reported experienced CQIs (Channel Quality Indicators). The manager intercepts a client's video stream request destined to the HTTP server, determines its group classification, and proceeds to transmit a properly encoded version of the requested stream. To effectively regulate inter-cell signal interference, we examine a number of different spectral reuse and fractional frequency reuse scheduling schemes. We calculate, for each scheduling and user group classification scheme, the average bandwidth per stream that is required to provide user groups with their targeted QoE levels. We demonstrate a user classification process that aims to optimize a utility metric based on the definition of a will-topay utility function. The derived optimal configuration of FFR based schemes are shown to significantly enhance the system's performance behavior.

Optimal Network-Assisted Multiuser DASH Video Streaming

“…From this perspective, our main intention with NS-3 simulations in this part of the paper is to illustrate the optimality gap between the BDRA algorithm and other selected rate-fair resource allocation schemes. As a result, with this intention in the paper, we only compare the performance of the BDRA algorithm with other potential resource allocation mechanisms whose operation does not require knowledge about either network throughput rates, or channel quality indicators, or detailed client operation as different from most existing work in the literature [15], [16], [31]- [37].…”

“…A similar framework is considered in [32], but with the performance objective being a fair QoE maximization rather than being the aggregate video utility maximization as in [31]. Similarly, network assisted video quality assignment and bandwidth allocation schemes for improving QoE in HTTP video streaming systems are also studied in some recent papers such as [16], [33] and [34]. The solutions in these papers require various side information to run properly such as network throughput rates, channel quality indicators, clients' instantaneous buffer states and clients' buffer occupancy trends.…”

“…When the instantaneous channel state information is available, this information can be further utilized to modify the scheduling algorithm in order to improve the network performance as in[16].…”

QoE-Driven UAV-Enabled Pseudo-Analog Wireless Video Broadcast: A Joint Optimization of Power and Trajectory

“…For example, in the UAV-enabled P2P video transmission scenario, the UAV usually stores multiple versions of the same video content encoded at different coding rates. The GU can only request the UAV for the video content which matches its instantaneous end-to-end throughput [28,29] or playback buffer status [30]. Switching between different video rates frequently inevitably causes visual quality fluctuations that affect the QoE [31,32].…”