## 2 Related Work  
As reported by Cisco [19], video data transmission has variable-bitrates. This results in periods of spikes in traffic followed by periods of low traffic transmission, and hence video traffic is considered as bursty. Conventionally, User Datagram Protocol (UDP) is more suitable for the task of video transmission. However as the type of video traffic became more dynamic with the introduction of new types of codecs and an increase in the user bandwidth and larger receiver buffers for accommodating extra TCP traffic, UDP has been replaced by TCP for new video applications like YouTube [20]. VoD applications working over both UDP and TCP show great dependence on the network QoS parameters like packet loss and jitter. As reported in [11; 12; 13; 14; 15], the increase in PLR and jitter results in degradation of video quality. Researchers in [11; 12] have demonstrated the effect of PLR on the QoE of VoD applications. Using subjective QoE evaluation, they concluded that the QoE of VoD degrades for increasing PLR levels. The author of [11] reported QoE as an exponential function of PLR and presented results for different age groups. Similarly, the authors of [12] used NetEm to evaluate the subjective QoE at different PLR levels. They reported QoE as a power-law function of PLR for MPEG-4 video codec over UDP. Moreover, [PERSON] and [PERSON] [14] reported how PLR and jitter affect different VoD frames, encoded using different codecs. Using recently developed codecs like VP-9 and H.265 and subjective testing, the authors of [14] demonstrated that QoE degrades for increasing PLR levels. They also reported QoE as exponential, logarithmic and polynomial functions of PLR. Similarly, the authors in [21] captured the data for different types of data traffic from mobile applications and presented the QoE of different application for different QoS parameters like PLR, delay and throughput. They also address the limitations of presenting QoE as Mean Opinion Score (MOS) and presented their results as the slope of the QoE for a certain QoS parameters and for different multimedia services. As reported in [14], the QoE for VoD using the VP-9 codec degrades rapidly for small increments in jitter, and the relationship was found to be an exponential function. In contrast the authors in [15] report that QoE showed less sensitivity to increasing jitter magnitudes and decays slowly when jitter was increased. They presented their subjective QoE as a power-law function of jitter. This difference shows that different codecs, testbeds and regression algorithms yield different QoE and jitter models. Moreover, ref. [22] studies the combined effect of PLR and jitter on QoE of VoIP applications. The authors map their subjectively acquired QoE to PLR and jitter using polynomial regression and presented their results as 1 st and 2 nd order polynomials. We tried to reproduce the results of the polynomials by using different operating points (commonly used QoS magnitudes)of PLR and jitter but the published polynomial results were not reproducible, and hence the model is not used in this research for further analysis. No similar attempt has been reported mapping QoE to a multi-variable function of both PLR and jitter for VoD in the literature. Subjective QoE is commonly evaluated using MOS [11; 12; 13; 14; 15]. The quantification of QoE as MOS has many limitations as highlighted by the authors in [23], and they represented QoE as Quantiles to elaborate the meaning of the perception to QoE evaluators. Evaluating perception just based on MOS is not necessarily representative as MOS represents a mean of the score and hides significant information about the underlying data set; hence presenting the QoE as Quantiles or distributions as done in [24] is more informational than using MOS alone. We propose a related idea here: to present the QoE with confidence intervals at certain QoS operating points for better understanding of perception of VoD applications and to overcome the limitations imposed by the wide use of MOS alone.