CFA Encoding Adaptation Mechanism

The encoding adaptation mechanism enables a video streaming server to adapt its source bit rate to cdma2000 radio link, whose bandwidth may vary in time due to system congestion or handoff. The benefits of the encoding adaptation mechanism are able to enhance system throughput by upgrading video resolution or to improve system capacity by supplying basic visual quality. Current adaptation mechanisms, such as the client based adaptation and the radio network feedback (RNF) adaptation, have their own superiorities and defects. Thus the CFA encoding adaptation mechanism is proposed to keep the superiorities and to avoid the defects from current mechanisms.

Client based adaptation mechanism consists of an event-triggered adaptation request from the client to the remote media server, and the front end wireless system is transparent.

To send an adequate adaptation request, the client continuously estimates the incoming throughput, filters out the variations due to radio link jitter and monitors buffer fullness. This mechanism implies a specific algorithm to be implemented in the client, and the proprietary protocol needs to be built to communicate the detected link rate from the client to the server transparently. Frequent adaptations of transmission bandwidth due to the trial and error algorithm may not only cause additional signaling overhead but also annoy the end user because of the unstable visual quality. Without a unified controller to examine the necessity of each request, clients themselves would arbitrarily adapt required bandwidth. This results in buffer underflow due to system overload and throughput degradation due to system idle.

Immediate feedback notification of the guaranteed radio link parameters is the main concept of RNF mechanism. Parameters such as the available bandwidth, the power budget, and the estimated RF condition can help the remote application server/proxy making stringent adaptation decision. The main advantage is also the major disadvantage of the RNF mechanism because the guarantee of sufficient radio resources drives conservative up-switch policy and sensitive down-switch policy. These policies sacrifice the chance to utilize good RF condition and to fight for more resource budgets. Thus performance of such mechanism is stable in visual quality but low utilization rate in system performance.

The lack of critical information is the reason why typical mechanisms can’t look after

both sides—user perceived visual quality and system performance. Without precise information about system available resources, the client based adaptation mechanism using trial and error algorithm may fit the maximum resource utilization rate but guarantee no QoS.

On the contrast, knowing nothing about the client, the lowest QoS is guaranteed but system performance is sacrificed. This is because the RNF adaptation mechanism tries to absorb any accidental deficiency and to control all possible risks.

The CFA adaptation mechanism exploits both system and client information as its decision parameters. These parameters are categorizes into historical record and current status.

The historical record needs to memorize recent schedules and allocations, and the current status needs to reflect client emergency and system congestion. Monitoring the recent schedules and the emergency index can the CFA adaptation mechanism grasp user perceived quality. Analyzing the recent allocations and the congestion index can the CFA adaptation mechanism estimate the system loading and the available resource budgets. These knowledge and estimations enable the CFA adaptation mechanism to precisely speculate adaptation necessity. So the CFA adaptation mechanism can improve system performance by permitting up-switch request instantly and can alleviate uncertainty by monitoring available resources.

The CFA adaptation mechanism defines two historical records— SCH and W . SCH is the estimated available bandwidth depending on recently assigned bandwidth, and it is the resultant affected by visual quality, system loading, and RF condition. Using SCH , the CFA adaptation mechanism can predict the risk of additional delay if the video stream with higher visual quality is sent. W is the mean of weighting factor “W” during contiguous adaptation and indicates the average starvation to radio resources. The CFA adaptation mechanism can predict whether a user will be in high priority in next second byW . The value of W , which also distributes into three separating groups since it comes from W, indicates the corresponding level of risk to up-switch the encoding bit rate, and indicates the corresponding emergency to down-switch the encoding bit rate.

The CFA adaptation utilizes five timing indices—tˆ^trans, tˆ^play, tˆ , ^stk t^preload,and t^dec to grasp current status, including resource emergency and system congestion. tˆ^transis the predicted transmit time, estimating how long the system needs to completely transmit backlog

in BSQ. tˆ^play is the predicted playout time, estimating how long the queued video packets in BSQ can be played on the client after being received and decoded. tˆ is the predicted stack ^stk time, estimating how long the packets not yet be decoded can be played. Without upper layer decapsulation, demultiplexing, and decoding, the precise encoding bit rate of each frame is not known, hence the CFA adaptation mechanism can only use current or average encoding bit rate to speculate. t^preload is the same as preload time Tpreload and is the precise index. t^decis the decoded time, representing how long the output buffer can sustain continuous playout until buffer underflow. It’s also a precise index because video frame rate (10 frames per second in our study) is predefined, and the number of decoded frame is actually known. These CFA adaptation parameters are calculated as figure 3-7 shown.

The design concept of the CFA adaptation mechanism is “judging whether the estimated available bandwidth is timely sufficient to avoid the client buffer underflow”.

Figure 3-7 and figure 3-8 shows the algorithm of the CFA adaptation mechanism. To declare how the encoding bit rate is adapted (up-switch, no-switch, or down-switch), the CFA adaptation mechanism first assigns an authority parameter q∈{-1,0,1}to each video session.

“q” releases specific authority for each video session to revise its encoding bit rate depending on the available resources. The smaller W , the more backlogs in the client buffer. This implies the confidence to suffer higher risk, so the timing constraint is relatively relaxed. On the contrast, high W implies no more risk should be suffered, and the crucial up-switch condition is set. The client who stays in good RF condition and has the authority parameter

“1” can enhance (up-switch) the encoding bit rate first. The client who gets authority parameter “0” may not enhance (up-switch) the encoding bit rate unless we can make sure that the system has sufficient resources and the capacity is not congested. The client who gets authority parameter “-1” will degrade (down-switch) the encoding bit rate to avoid the risk of buffer underflow due to channel variation, sudden overload, and etc.

To improve client visual quality or system throughput, the media server up-switches the encoding bit rate if the sufficient backlog in the client buffer and the available resources can guarantee no buffer underflow. The media server down-switches the encoding bit rate if the limited radio resources can’t guarantee a timely transmission following current encoding bit rate. In this algorithm, two kinds of threshold are used—t^preload−t^dec−tˆ^stk and tˆ^play. They have analogous physical meaning but are considered from different point of view. Since the

major risk of up-switching the encoding bit rate stems from the buffer underflow, estimating the required transmission time by inspecting the buffer fullness through t^preload−t^dec−tˆ^stk is more meaningful and correct. On the other hand, the only reason a video session needs to down-switch its encoding bit rate is the lack of the radio resources. Thus estimating the required transmission time by inspecting the queued video packets (tˆ^play) in BSQ is the most accurate. Finally, combining the authority parameter “q” and the available resources, the system can decide whether the adaptation should be executed.

Figure 3-7 representation of the timing indices

Figure 3-8 CFA adaptation algorithm

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