Multimedia QoE optimized management using prediction and statistical learning

Abstract

Abstract-We present a scheme for flow management with heterogeneous access technologies available indoors and in a campus network such as GPRS, 3G and Wi-Fi. Statistical learning is used as a key for optimizing a target variable namely video quality of experience (QoE). First we analyze the data using passive measurements to determine relationships between parameters and their impact on the main performance indicator, video Quality of Experience (QoE). The derived weights are used for performing prediction in every discrete time interval of our designed autonomic control loop to know approximately the QoE in the next time interval and perform a switch to another access technology if it yields a better QoE level. This user-perspective performance optimization is in line with operator and service provider goals. QoE performance models for slow vehicular and pedestrian speeds for Wi-Fi and 3G are derived and compared.

Survey on Qoe\Qos Correlation Models Formultimedia Services

“…Mok et al [23] -The rebuffering frequency is the important factor responsible for the QoE variance -Temporal structure, instead of spatial artifacts, is an important factor affecting the QoE. Elkotob et al [32] -The proposed scheme allows a mobile node to be proactively aware of the best access network for the next interval. -This scheme would be interesting for operators and service providers who need to maintain graceful QoE profiles and optimize their resource usage.…”

“…al [33] × × × × Machado et al [37] × × × × Du et al [39] × × Kim et al [21] × × × Khan et al [42] × × Malinovski et al [44] × × × × Han et al [50] × × × × × × Laghari et al [51] × × × Ramos et al [28] × × × Koumaras et al [22] × × × Frank et al [40] × × × Calyam et al [41] × × × × × × Mok et al [23] × × Elkotob et al [32] × × × × × × Mushtaq et al [38] × × × × Hoßfeld et al [45] × × × ×…”

Survey on machine learning-based QoE-QoS correlation models

“…Furthermore, depending on the QoE values type, offline batch models can be divided into two groups. The first group uses the regression analysis to approximate the QoE as a continuous function of QoS parameters like in [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24].The second group uses classification methods to predict the QoE class [25][26][27][28][29]. In the following sections, we briefly describe these models.…”

Challenges and opportunities in content distribution networks: A case study

“…For QoE modeling, the Me used; this metric is explaine example models for video are p QoE is taken as the linear w he last mile or access network n in Figure 1 is Table 1 and some QoE provided in [22]. For this model weighted sum of TTFB, delay bound, and packet loss rate (PLR) with all coefficients being equal and the result normalized to fit the MOS scale of 1-5.…”