TY - JOUR
T1 - QoE-Traffic Optimization Through Collaborative Edge Caching in Adaptive Mobile Video Streaming
AU - Mehrabidavoodabadi, Abbas
AU - Siekkinen, Matti
AU - Ylä-Jääski, Antti
N1 - This work was supported in part by the Academy of Finland under Grant 278207 and Grant 297892, in part by the Tekes - the Finnish Funding Agency for Innovation, and in part by the Nokia Center for Advanced Research.
PY - 2018/10/10
Y1 - 2018/10/10
N2 - Multi-access edge computing has been proposed as a promising approach to localize the access of mobile clients to the network edges, therefore, reducing significantly the traffic congestion on the backhaul network. Due to time-varying wireless channel condition, the video caching at the mobile edges for dynamic adaptive video streaming over HTTP (DASH) needs to be efficiently handled to alleviate the high bandwidth demand on the backhaul network and improve the quality of experience (QoE) of end users. We investigate the impact of collaborative mobile edge caching on joint QoE and backhaul data traffic by proposing the joint QoE-traffic optimization with collaborative edge caching which introduces the BFTR (backhaul/fronthaul traffic ratio) parameter adjustable by the mobile network operator. We then design a self-tuned bitrate selection algorithm with low complexity to solve the optimization problem and further propose an efficient cache replacement strategy called retention-based collaborative caching. Through simulation-based evaluations, we show a noticeable gain in the percentage of cache miss and specify some threshold for BFTR parameter after which the significant reduction in the data traffic with further improvement in average video bitrate is obtained using collaborative caching. Our findings help mobile edge system developers design an efficient collaborative caching mechanism for 5G networks.
AB - Multi-access edge computing has been proposed as a promising approach to localize the access of mobile clients to the network edges, therefore, reducing significantly the traffic congestion on the backhaul network. Due to time-varying wireless channel condition, the video caching at the mobile edges for dynamic adaptive video streaming over HTTP (DASH) needs to be efficiently handled to alleviate the high bandwidth demand on the backhaul network and improve the quality of experience (QoE) of end users. We investigate the impact of collaborative mobile edge caching on joint QoE and backhaul data traffic by proposing the joint QoE-traffic optimization with collaborative edge caching which introduces the BFTR (backhaul/fronthaul traffic ratio) parameter adjustable by the mobile network operator. We then design a self-tuned bitrate selection algorithm with low complexity to solve the optimization problem and further propose an efficient cache replacement strategy called retention-based collaborative caching. Through simulation-based evaluations, we show a noticeable gain in the percentage of cache miss and specify some threshold for BFTR parameter after which the significant reduction in the data traffic with further improvement in average video bitrate is obtained using collaborative caching. Our findings help mobile edge system developers design an efficient collaborative caching mechanism for 5G networks.
KW - Collaborative caching
KW - dynamic adaptive video streaming over HTTP (DASH)
KW - fairness
KW - integer non-linear programming
KW - multi-access edge computing (MEC)
KW - NP-hardness
KW - quality of experience
U2 - 10.1109/ACCESS.2018.2870855
DO - 10.1109/ACCESS.2018.2870855
M3 - Article
VL - 6
SP - 52261
EP - 52276
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -