Quality of Experience Enhancement Architecture For Video Delivery On WiFi
AdvisorFapojuwo, Abraham O.
Committee MemberBehjat, Laleh
Dimitrov, Vassil S.
Smith, Michael Richard
Fapojuwo, Abraham O.
ClassificationEngineering--Electronics and Electrical
MetadataShow full item record
AbstractWiFi has become a widely used medium for delivery of video content. At the same time, there is a proliferation of WiFi capable devices such as mobile phones and portable computers with the capability of consuming video content using WiFi as the access technology. Due to the proliferation of such devices, there is a probability of reduced video Quality of Experience (QoE) due to wireless channel impairments. Wireless channel impairments are a challenging problem for WiFi which uses the license-free ISM band. Even though most devices using the WiFi frequencies will adhere to the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol, there are other devices which do not use this protocol and thus cause interference. Another issue that impairs the wireless channel is reduced Signal to Interference plus Noise Ratio (SINR) due to the access point (AP) to user equipment (UE) distance, or obstruction. Also, when the number of users in a radio range increase, there is an increased probability of collisions. These impairment mechanisms will consequently reduce the video QoE served to a user. The QoE is reduced because of the need for retransmissions, which reduces the channel throughput. Also, because of impairments, the modulation and coding scheme (MCS) selected by the rate adaptation algorithm for WiFi is restricted to combinations which are more robust against these impairments. To improve the QoE served to a user, this research pursues two main approaches: (1) Evaluate methods of estimating QoE for streaming video (2) Use a Rate Adaptation (RA) algorithm in conjunction with Application Layer Forward Error Correction (AL-FEC) to improve air efficiency. This research utilizes a mathematical model which considers the reduced video resolution during impairments, and effects of stalled video, on the QoE. To reduce the effects of retransmissions, and increase the air efficiency, this study investigates the use of Application Layer Forward Error Correction (AL-FEC), in coordination with Rate Adaptation. FEC schemes are investigated, and a theoretical and experimental evaluation of a AL-FEC scheme is validated, for each MCS selection. Further, a scheme is proposed whereby the wireless channel is regarded as the lowest bandwidth of the path between the video source and consumer. The video source will signal to the WiFi AP for the desired MCS, based on the AL-FEC capabilities, and AL-FEC success rate at the user. The desired MCS is determined by the user and sent via a feedback channel to the video source. The rate adaptation algorithm is evaluated using a Markov model to estimate the gains achieved when using AL-FEC aware Rate Adaptation. Finally an end-to-end algorithm is proposed which selects the optimal AL-FEC scheme based on the current channel conditions. The QoE is compared for critical insight into the gains obtained when using the AL-FEC aware Rate Adaptation Algorithm, and AL-FEC, in order to improve the user Quality of Experience.
CitationOsunkunle, I. (2020). Quality of Experience Enhancement Architecture For Video Delivery On WiFi (Unpublished doctoral thesis). University of Calgary, Calgary, AB.
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