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Xiaoqing Zhu

Publications and source records attributed to Xiaoqing Zhu.

21 records · Page 2Linked to original sources

Rate Adaptation and Admission Control for Video Transmission with Subjective Quality Constraints

Adapting video data rate during streaming can effectively reduce the risk of playback interruptions caused by channel throughput fluctuations. The variations in rate, however, also introduce video quality fluctuations and thus potentially affects viewers' Quality of Experience (QoE). We show how the QoE of video users can be improved by rate adaptation and admission control. We conducted a subjective study wherein we found that viewers' QoE was strongly correlated with the empirical cumulative distribution function (eCDF) of the predicted video quality. Based on this observation, we propose a rate-adaptation algorithm that can incorporate QoE constraints on the empirical cumulative quality distribution per user. We then propose a threshold-based admission control policy to block users whose empirical cumulative quality distribution is not likely to satisfy their QoE constraint. We further devise an online adaptation algorithm to automatically optimize the threshold. Extensive simulation results show that the proposed scheme can reduce network resource consumption by $40\%$ over conventional average-quality maximized rate-adaptation algorithms.

cs.MM↗

Probe and Adapt: Rate Adaptation for HTTP Video Streaming At Scale

Today, the technology for video streaming over the Internet is converging towards a paradigm named HTTP-based adaptive streaming (HAS). HAS comes with two unique flavors. First, by riding on top of HTTP/TCP, it leverages the network-friendly TCP to achieve firewall/NATS traversal and bandwidth sharing. Second, by pre-encoding and storing the video in a number of discrete bitrate levels, it introduces video bitrate adaptivity in a scalable way that the video encoding is excluded from the closed-loop adaptation. A conventional wisdom is that the TCP throughput observed by a HAS client indicates the available network bandwidth, thus can be used as a reliable reference for the video bitrate selection. We argue that this no longer holds true when HAS becomes a substantial fraction of the Internet traffic. We show that when multiple HAS clients compete at a network bottleneck, the presence of competing clients and the discrete nature of the video bitrates would together create confusion for a client to correctly perceive its fair-share bandwidth. Through analysis and real experiments, we demonstrate that this fundamental limitation would lead to, for example, video rate oscillation that negatively impacts the video watching experiences. We therefore argue that it is necessary to implement at the application layer a "probe-and-adapt" mechanism for HAS video rate adaptation, which is akin but orthogonal to the transport-layer network rate adaptation achieved by TCP. We present PANDA -- a client-side rate adaptation algorithm for HAS -- as an embodiment of this idea. Our testbed results show that compared to conventional algorithms, PANDA is able to reduce the instability of video rate by 60%, at a given risk of buffer underrun.

cs.NI↗

Distributed Rate Allocation Policies for Multi-Homed Video Streaming over Heterogeneous Access Networks

We consider the problem of rate allocation among multiple simultaneous video streams sharing multiple heterogeneous access networks. We develop and evaluate an analytical framework for optimal rate allocation based on observed available bit rate (ABR) and round-trip time (RTT) over each access network and video distortion-rate (DR) characteristics. The rate allocation is formulated as a convex optimization problem that minimizes the total expected distortion of all video streams. We present a distributed approximation of its solution and compare its performance against H-infinity optimal control and two heuristic schemes based on TCP-style additive-increase-multiplicative decrease (AIMD) principles. The various rate allocation schemes are evaluated in simulations of multiple high-definition (HD) video streams sharing multiple access networks. Our results demonstrate that, in comparison with heuristic AIMD-based schemes, both media-aware allocation and H-infinity optimal control benefit from proactive congestion avoidance and reduce the average packet loss rate from 45% to below 2%. Improvement in average received video quality ranges between 1.5 to 10.7 dB in PSNR for various background traffic loads and video playout deadlines. Media-aware allocation further exploits its knowledge of the video DR characteristics to achieve a more balanced video quality among all streams.

cs.MM↗