Optimal DASH Video Scheduling over Variable-Bit-Rate Networks
Shin-Hung Chang, Kuan-Jen Wang, Jan-Ming Ho · 2018
To accommodate users' heterogeneous network bandwidth usage, current video-service providers, e.g., YouTube and Netflix, generally use DASH technology to deploy their streaming services. A HTTP server handles client-server negotiation and dynamically adjusts a series of specific video-streaming rates to each client according to their bandwidth requirements. The QoE (Quality of Experience) is an index of users' subjective opinions; we follow previous researchers' quantized QoE indices and introduce an extra index, called lexicographical minimization, as a measure of quality of a DASH video playback schedule, abbreviated as DASH scheduling, given the available network bandwidth. A video playback schedule with minimum low-resolution video segments is said to have the best QoE. This paper presents a linear time M-Low scheduling algorithm, which adjusts the video resolution and optimizes the QoE indices in a DASH streaming service. We refer to the following QoE metrics: playback without introducing freezes, minimizing low-resolution video segments, minimizing the number of resolution-switching events, and maximizing video playback bitrate. We prove that the playback schedule generated by the M-Low algorithm is lexicographically minimal when the network bandwidth is in a steady state. Moreover, we extend M-Low algorithm to M-LowS algorithm over Variable-Bit-Rate (VBR) networks and show through simulations that our proposed algorithm achieves better QoE indices than those of previously published algorithms.