# Fuzzy-Based Quality Adaptation Algorithm for Improving QoE from MPEG-DASH Video
## 2 Related Work  
Over the last decade, multiple ABR schemes have been proposed to improve QoE. The ABR algorithms can be divided into three methods: (1) throughput-based, (2) buffer-based, and (3) hybrids. Throughput-based algorithms pick the video rate of the segments considering the available throughput, \(T(i)\), observed during the download of the \(i\)th segment [4; 14; 15; 16; 17]. In an unstable network, the available throughput may fluctuate quickly. Because the video rate is selected based on throughput, an unstable network would lead to more video rate switches, which degrades the user experience. In order to minimize video rate-switching frequency, the smoothed throughput measure, \(T^{\mathrm{s}}(i)\), is employed:  
\[T^{\mathrm{s}}\left(i+1\right)=\left(1-\delta\right)\times T^{\mathrm{s}}(i)+ \delta\times T(i) \tag{1}\]  
where the value of weighing factor \(\delta\) is within the range [0, 1]. The authors in [18] provided pseudocode for the adaptation algorithm of Google's MPEG-DASH Media Source demo. The algorithm selects video rates only based on the throughput observed during download of the segments. The algorithm uses a moving average of two different bandwidth estimation coefficients to reflect small- and large-scale bandwidth fluctuations. It then selects the smaller of the two as the bandwidth estimate for the next segment. Finally, the algorithm selects the highest available video rate that is lower than the bandwidth estimate. Because adaptation algorithms ignore buffer level, selecting video rates based on smoothed throughput increases the risk of playback interruption. [PERSON] et al. [19] showed that the throughput-based schemes cannot accurately estimate bandwidth when multiple clients compete for access through a network bottleneck. This leads to degradation in the selected video rates, unfair and inefficient utilization of bandwidth, and a higher frequency of video rate switches. Some ABR algorithms observe only the playback buffer to select the video quality [20; 21; 22; 23]. [PERSON] et al. [6] proposed a buffer-based algorithm (BBA) that divides the playback buffer into multiple predefined thresholds (\(B_{1}\), \(B_{2}\), \(B_{3}\), and \(B_{max}\)) such that \(B_{1}<B_{2}<B_{3}<B_{max}\). The algorithm respectively increases or decreases the video rate aggressively or conservatively based on whether the playback buffer level increases above the next higher buffer threshold or decreases below the next lower buffer threshold. In [21],the authors formulated video rate adaptation as a utility maximization problem. They devised an online algorithm called BOLA that uses Lyapunov optimization to improve the user experience. Multiple researchers have used a combination of playback buffer and throughput to select video quality [7; 8; 11; 23; 24; 25; 26; 27; 8]. Such algorithms combine the approaches used by throughput-based and buffer-based algorithms to select the quality of the next segment. [PERSON] et al. [25] proposed the Fuzzy-based DASH (FDASH) adaptation algorithm, which considers buffer level and variations in its level to mitigate playback interruption. The authors in [26] extended the work in [25] and also considered the available power in the selection of the segments in order to extend the lifetime of mobile video clients. [PERSON] et al. [26] modified FDASH by employing history-based throughput estimation, a segment bit-rate filtering module (SBFM), and a _Start Mechanism_ to improve the user experience. In [28], the authors used segment size in addition to throughput and the playback buffer size for video rate adaptation. [PERSON] et al. [29] proposed a fuzzy-based adaptation controller for DASH that takes observed throughput and playback buffer as input to pick the video rates. Multiple studies have shown that when multiple DASH clients compete for bandwidth in a wired network, it results in unfair allocation of bandwidth due to ON-OFF generated traffic [4; 6].