Network Setup: We use the Helix server version 9.0.2  as our streaming media server and use Mplayer  as the streaming media client. All streaming media requests are issued using the Real Time Streaming Protocol (RTSP) to stream packets using UDP. We built a client proxy and a server proxy to interpret the streaming media packets and associate them with different priorities. Using these proxies, we tunnel a media stream from RealServer to an Mplayer client along an overlay path along which we replay sample bursty loss traces collected along different overlay links. For the purpose of illustration, we consider two such loss traces: (a) Mazu (Boston)-Korea with an average loss rate of ; (b) Intel (San Francisco) - Lulea (Sweden) with an average loss trace of . Each trace is minutes long. To emulate the behavior without OverQoS, we consider the OverQoS nodes to act as packet forwarders. If the length of a media stream is shorter than the length of the trace, we repeat the analysis for different portions of the trace.
Streaming Audio: To demonstrate the effect of smooth dropping on streaming audio, we concatenated several speech samples provided by International Telecommunication Union (ITU-T) to produce two test samples of length sec and sec respectively. Perceptual Evaluation of Speech Quality(PESQ)  is one metric to evaluate the quality of voice. We measured the PESQ score for the received stream in comparison to the original stream. A PESQ score of is considered to be ideal implying that the received audio stream has not degraded in quality.1
Table 2 compares the PESQ scores of streaming audio with and without OverQoS for two benchmark speech samples. We observe that smoothing the losses does help in increasing the quality of the audio stream. Using OverQoS we are able to increase the PESQ score of the output stream by roughly . To demonstrate that is indeed a reasonable improvement in the audio quality, we experimented with several artificial bursty loss patterns while maintaining the same average loss-rate of the traces (i.e., and ) and measured the PESQ scores for each of them. For an average loss rate of , we found the PESQ scores to vary between and across a variety of bursty loss patterns. For these cases, we again found that smooth dropping performs better than bursty drops. Hence, we find that smoothing losses using OverQoS uniformly outperforms different types of bursty network losses.
MPEG streaming: Peak Signal-to-Noise ratio (PSNR) is a standard metric used to measure the quality of the video images in a stream. Given an MPEG stream received at the client, we use the ``-yuv4mpeg'' utility in Mplayer to convert the stream into a stream of images. For every image, we compute the PSNR value of the received image in comparison to the video image in the original MPEG stream. We quantify the quality of the received MPEG stream using a distribution of PSNR values for the individual images. We consider a sample MPEG-1 stream which is seconds for this analysis.
Table 3 compares the and median values of the PSNR values of the received MPEG stream with and without OverQoS across both the loss samples. We make the following observations. First, in the case when an entire I-frame was lost due to a burst, Mplayer stopped playing the video stream since an entire GOP cannot be reconstructed. This occurred in both the loss traces when a burst coincided with the packets of an I-frame. However, OverQoS was able to recover from the burst so that the stream could progress. Second, OverQoS is able to improve both the and the median PSNR values of the stream by preferentially dropping B and P packets in a burst when compared to the quality of the stream without OverQoS. We illustrate the PSNR value mainly to show that OverQoS not only improves the quality of the stream in the average case but also the minimum quality of a stream. To summarize, OverQoS can improve the quality of a media stream without consuming any additional network resources.