Quality-Control Algorithm for Adaptive Streaming Services Over Wireless Channels

Abstract

Abstract-Dynamic Adaptive Streaming over HTTP (DASH) is a recent MPEG standard for IP video delivery whose aim is the convergence of existing adaptive-streaming proprietary solutions. However, it does not impose any adaptation logic for selecting the quality of the media segments requested by the client, which is crucial to cope effectively with bandwidth fluctuations, notably in wireless channels. We therefore propose a solution to this control problem through Stochastic Dynamic Programming (SDP). This approach requires a probabilistic characterization of the system, as well as the definition of a cost function that the control strategy aims to minimize. This cost function is designed taking into account factors that may influence the quality perceived by the users. Unlike previous works, which compute control policies online by learning from experience, our algorithm solves the control problem offline, leading promptly to better results. In addition, we compared our algorithm to others during a streaming simulation and we analyzed the objective results by means of a Quality of Experience (QoE) oriented metric. Moreover, we conducted subjective tests to complete the evaluation of the performance of our algorithm. The results show that our proposal outperforms the other approaches in terms of both the QoE-oriented metric and the subjective evaluation.Index Terms-Adaptive streaming, DASH, quality of experience, stochastic dynamic programming.

SDP-Based Quality Adaptation and Performance Prediction in Adaptive Streaming of VBR Videos

“…We also compare our proposed method with two existing ones, namely, the SDP method presented [10] which could obtain the best performance among the SDP methods and the bitrate estimation based method presented [14], which is the best among the qualitative methods.…”

“…However, these existing methods mostly focus on constant bitrate (CBR) videos. The authors in [10] are the first to propose an adaptation algorithm in which stochastic dynamic programming (SDP) is employed to find optimal decision policies when streaming VBR videos. The segment requests are ruled by the policies which map a control parameter to every possible state of the system; however, it is limited to videos with weak bitrate fluctuations.…”

“…Nevertheless, they have not considered the effect of channel-state aware adaptation. Meanwhile, García et al [10] construct an infinite horizon problem and apply SDP to solve it specifically for HTTP adaptive streaming. They propose a channel model in which transitions are only possible to adjacent states, with equal probabilities, which is only suitable for the stable bandwidth, with little fluctuation.…”

“…The value of each bandwidth state is the average of the maximum value and minimum value of the corresponding bandwidth level. To represent the transition from one bandwidth state to another on a bandwidth state space, we use the Markov chain model which has been used widely in previous studies [10,24,25]. Figure 3 presents a general Markov-chain model which consists of three bandwidth states.…”

Q-learning based control algorithm for HTTP adaptive streaming

“…Other research studies model the problem as an optimization control problem and use mathematical tools to solve it. The solution proposed by Garcia [4] is based on Stochastic Dynamic Programming (SDP). They compute off-line optimal control policies based on a system model that is estimated apriori.…”