TY - JOUR
T1 - Fine-grained scalable video caching for heterogeneous clients
AU - Liu, J.
AU - Xu, J.
AU - Chu, X.
N1 - Funding Information:
Manuscript received January 12, 2005; revised November 4, 2005. An earlier version of this paper was published in IEEE INFOCOM’04. The work of J. Liu was supported in part by a Canadian NSERC Discovery Grant 288325, an NSERC Research Tools and Instruments Grant, a Canada Foundation for Innovation (CFI) New Opportunities Grant, and an SFU President’s Research Grant. The work of J. Xu work was supported in part by grants from the Research Grants Council of the Hong Kong SAR, China (Projects HKBU 2115/05E and HKBU FRG/03-04/II-19). The work of X. Chu was supported by RGC HKBU2159/04E and HKBU210605. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Pascal Frossard.
PY - 2006/10
Y1 - 2006/10
N2 - Much research has focused on caching adaptive videos to improve system performance for heterogeneous clients with diverse access bandwidths. However, existing rate-adaptive caching systems, which are based on layered coding or transcoding, often suffer from a coarse adaptation and/or a high computation overhead. In this paper, we propose an innovative rate-adaptive caching framework that enables low-cost and fine-grained adaptation by using MPEG-4 fine-grained scalable videos. The proposed framework is both network-aware and media-adaptive; i.e., the clients can be of heterogeneous streaming rates, and the backbone bandwidth consumption can be adaptively controlled. We develop efficient cache management schemes to determine the best contents to cache and the optimal streaming rate to each client under the framework. We demonstrate via simulations that, compared to nonadaptive caching, the proposed framework with the optimal cache management not only achieves a significant reduction in the data transmission cost, but also enables a flexible utility assignment for the heterogeneous clients. Our results also show that the framework maintains a low computational over-head, which implies that it is practically deployable.
AB - Much research has focused on caching adaptive videos to improve system performance for heterogeneous clients with diverse access bandwidths. However, existing rate-adaptive caching systems, which are based on layered coding or transcoding, often suffer from a coarse adaptation and/or a high computation overhead. In this paper, we propose an innovative rate-adaptive caching framework that enables low-cost and fine-grained adaptation by using MPEG-4 fine-grained scalable videos. The proposed framework is both network-aware and media-adaptive; i.e., the clients can be of heterogeneous streaming rates, and the backbone bandwidth consumption can be adaptively controlled. We develop efficient cache management schemes to determine the best contents to cache and the optimal streaming rate to each client under the framework. We demonstrate via simulations that, compared to nonadaptive caching, the proposed framework with the optimal cache management not only achieves a significant reduction in the data transmission cost, but also enables a flexible utility assignment for the heterogeneous clients. Our results also show that the framework maintains a low computational over-head, which implies that it is practically deployable.
KW - Fine-grained scalable video
KW - Proxy caching
KW - Resource allocation
KW - Streaming media
UR - http://www.scopus.com/inward/record.url?scp=33749530191&partnerID=8YFLogxK
U2 - 10.1109/TMM.2006.879859
DO - 10.1109/TMM.2006.879859
M3 - Journal article
AN - SCOPUS:33749530191
SN - 1520-9210
VL - 8
SP - 1011
EP - 1019
JO - IEEE Transactions on Multimedia
JF - IEEE Transactions on Multimedia
IS - 5
M1 - 1703515
ER -