Effective Quality-of-Service Renegotiating Schemes for Streaming Video
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Effective Quality-of-Service Renegotiating Schemes for Streaming Video Hwangjun Song School of Electrical Engineering, Hongik University, 72-1 Sangsu-dong, Mapo-gu, Seoul 121-791, Korea Email: [email protected]
Dai-Boong Lee School of Electrical Engineering, Hongik University, 72-1 Sangsu-dong, Mapo-gu, Seoul 121-791, Korea Email: [email protected] Received 13 November 2002; Revised 25 September 2003 Effective quality-of-service renegotiating schemes for streaming video is presented. The conventional network supporting quality of service generally allows a negotiation at a call setup. However, it is not efficient for the video application since the compressed video traffic is statistically nonstationary. Thus, we consider the network supporting quality-of-service renegotiations during the data transmission and study effective quality-of-service renegotiating schemes for streaming video. The token bucket model, whose parameters are token filling rate and token bucket size, is adopted for the video traffic model. The renegotiating time instants and the parameters are determined by analyzing the statistical information of compressed video traffic. In this paper, two renegotiating approaches, that is, fixed renegotiating interval case and variable renegotiating interval case, are examined. Finally, the experimental results are provided to show the performance of the proposed schemes. Keywords and phrases: streaming video, quality-of-service, token bucket, renegotiation.
1.
INTRODUCTION
In recent years, the demands and interests in networked video have been growing very fast. Various video applications are already available over the network, and the video data is expected to be one of the most significant components among the traffics over the network in the near future. However, it is not a simple problem to transmit video traffics efficiently through the network because the video requires a large amount of data compared to other multimedia. To reduce the amount of data, it is indispensable to employ effective video compression algorithms. So far, digital video coding techniques have advanced rapidly. International standards such as MPEG-1, MPEG-2 [1], MPEG-4 [2], H.261 [3], H.263/+/++ [4], H.26L, and H.264 have been established or are under development to accommodate different needs by ISO/IEC and ITU-T, respectively. The compressed video data is generally of variable bit rate due to the generic characteristics of entropy coder and scene change inconsistent motion change of the underlying video. Furthermore, video data is time constrained. These facts make the problem more challenging. By the way, constant bit rate video traffic can be generated by controlling the quantization parameters and it is much easier to handle over the network, but the quality of the decoded video may be seriously degraded.
In general, suitable communications between the network and the sender end can increase the network utilization and enhance video quality at the receiver end simultaneously [5]. Generally speaking, the variability of compressed
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