A quality of experience approach in smartphone video selection framework for energy efficiency

Online video streaming is getting more common in the smartphone device nowadays. Since the Corona Virus (COVID-19) pandemic hit all human across the globe in 2020, the usage of online streaming among smartphone user are getting more vital. Nevertheless, video streaming can cause the smartphone energy to drain quickly without user to realize it. Also, saving energy alone is not the most significant issues especially if with the lack of attention on the user Quality of Experience (QoE). A smartphones energy management is crucial to overcome both of these issues. Thus, a QoE Mobile Video Selection (QMVS) framework is proposed. The QMVS framework will govern the tradeoff between energy efficiency and user QoE in the smartphone device. In QMVS, video streaming will be using Dynamic Video Attribute Pre-Scheduling (DVAP) algorithm to determine the energy efficiency in smartphone devices. This process manages the video attribute such as brightness, resolution, and frame rate by turning to Video Content Selection (VCS). DVAP is handling a set of rule in the Rule Post-Pruning (RPP) method to remove an unused node in list tree of VCS. Next, QoE subjective method is used to obtain the Mean Opinion Score (MOS) of users from a survey experiment on QoE. After both experiment results (MOS and energy) are established, the linear regression technique is used to find the relationship between energy consumption and user QoE (MOS). The last process is to analyze the relationship of VCS results by comparing the DVAP to other recent video streaming applications available. Summary of experimental results demonstrate the significant reduction of 10% to 20% energy consumption along with considerable acceptance of user QoE. The VCS outcomes are essential to help users and developer deciding which suitable video streaming format that can satisfy energy consumption and user QoE

Network Efficient Resource Management for Mobile Video Streaming based on Quality of Experience

This paper presents a novel experimental approach to quantify the performances of Quality of Experience (QoE)- aware resource management scheme in mobile network. The main goal of this paper is to improve network efficiency by exploiting knowledge of QoE information associated with online video streaming services. The investigations considered in the paper are performed using an innovative test-bed, developed to assess network efficiency for the provision of online video services of different qualities. The QoE model used in the proposed QoEaware allocation scheme assumes a MOS-like grading function whose grades depend on both the duration of playtime interruption and the streaming video quality (resolution). The results show that the proposed resource management scheme can deliver more than 40 percent higher QoE to the users of the system as compared to current agnostic (not aware of QoE requirement and content characteristics) service models.QC 20131125</p