LAYERED QUALITY ADAPTATION for INTERNET VIDEO STREAMING Reza Rejaie, Mark Handley and Deborah Estrin Information Science Institute (ISI), University of Southern California by IEEE Journal On Selected Areas In Communications Vol. 18., NO.12, December 2000 published in

- 2 types of flows: TCP vs. UDP - TCP: responsive to the congestion : reliable (provide retransmission) - UDP: non-responsive : non-reliable Review:

Problem: - Explosive growth of audio & video streaming - Streaming applications: rate-based, delay-sensitive, semi-reliable - Lack of effective congestion control mechanism in such applications

To support streaming applications over the Internet, two conflicting requirements have to be addressed: - Application Requirement * require relatively constant BW to deliver a stream with a certain quality - Network Requirement * end systems are expected to react to congestion properly and promptly

To satisfy these two requirements simultaneously, Internet streaming quality adaptive applications should be quality adaptive - the application should adjust the quality of the delivered stream such that the required BW matches the congestion controlled rate-limit - the main challenge is to minimize the variations in quality, while obeying the congestion controlled rate-limit

This paper presents: novel quality adaptation mechanism - the novel quality adaptation mechanism which can adjust the quality of congestion controlled video playback on-the-fly - the key feature of this mechanism is the ability to control the level of frequency of changes smoothing (i.e., frequency of changes) to improve quality of the delivered stream

Primary Assumption: Rate - the congestion control protocol is Rate Adaptation Protocol (RAP) - RAP is a rate-based congestion control mechanism that employs an Additive Increase Multiplicative Decrease (AIMD) algorithm in a manner similar to TCP

RAP Increase/Decrease Algorithm Base Equation: S i = PacketSize / IPG i To increase the rate additively, IPG i+1 = (IPG i * C) / (IPG i + C) where C is a constant with the dimension of time Upon detecting congestion, the tx rate is decreased multiplicatively, Si+1 = * Si, IPGi+1 = IPGi / where = 0.5   

RAP Decision Frequency - how often to change the rate feedback delay - depends on the feedback delay - feedback delay in ACK-based schemes is equal to one RTT - it is suggested that rate-based schemes adjust their rates not more than once per RTT

remote video server INTERNET Target Environment Client#1 Client#2 Client#3 Client#4 - heterogeneous clients - Video on demand - dominant competing traffic is TCP - short startup latency expected

Quality Adaptation Mechanisms - “Adaptive Encoding”: requantize stored encoding on-the-fly based on the network feedback disadv: CPU-intensive task - “Switching among multiple pre-encoded versions”: the server keeps several versions of each stream with different qualities. As available BW changes, the server chooses the appropriate version of the stream disadv: need large server’s buffer

- “Hierarchical Encoding”: the server maintains a layered encoded version of each stream. As more BW is available, more layers of the encoding are delivered. If the available BW decreases, the server may then drop some of the layers being transmitted *** layered approaches usually have the decoding constraint that a particular enhancement layer can only be decoded if all the lower quality layers have been received

adv:- less storage at the server - provides an opportunity for selective selective repair of the more important information *** the design of a layered approach for quality adaptation primarily entails the design of an efficient add and drop mechanism that maximizes quality while minimizing the probability of base-layer buffer underflow

NOTE ! - Hierarchical Encoding provides an effective way for a video server to coarsely without adjust the quality of a video stream without transcoding transcoding the stored data - However, it does not provide the fine-grained control fine-grained control over BW, that is, BW only changes at the granularity of a layer

- there needs to be a quality adaptation mechanism with the ability to control the level of smoothing “short-term improvement” vs. “long-term smoothing”

aggressive approach - in the aggressive approach, a new layer is added as a result of minor increase in available BW. However, this additional bandwidth does not last long. Thus, the short-term improvement aggressive approach results in short-term improvement conservative approach - in contrast, the conservative approach does not adjust the quality due to the minor changes in BW. Thus, this long-term smoothing results in long-term smoothing

- Hierarchical encoding allows video quality adjustment over long periods of time, whereas congestion control changes the tx rate rapidly over short time intervals - The mismatch between the two buffering data time scales is made up for by buffering data at the receiver at the receiver to smooth the rapid variations in available BW and allow a near constant number of layers to be played

The Proposed Architecture - all the streams are layered-encoded and stored at the server - all active layers are multiplexed into a single RAP flow by the server

- at the client side, layers are de-multiplexed and each one goes to its corresponding buffer - the decoder drains data from buffers and feeds the display - assume that, each layer has the same BW and all buffers are drained with the same constant rate (C) - the congestion control module continuously reports available BW to the quality adaptation module - the quality adaptation module then adjusts # of active layers and allocated share of congestion controlled BW to each active layer

Challenging Questions: - When is it suitable to add a new layer ? - When should we drop a layer ? - What is the optimal allocation of each layer ? Assumption Assumption: - would like to survive a single backoff with all the layers intact

Adding a Layer: The server may add a new layer when: 1.the instantaneous available BW is greater than the consumption rate of the existing layers plus the new layer R > (n a + 1) C, and

2. there is sufficient total buffering at the receiver to survive an immediate backoff and continue playing all the existing layers plus the new layer i=0 to na –1 buf i ([n a + 1] C – [R/2]) 2 2*S   where R: the current tx rate n a : the number of currently active layer buf i : the amount of buffered data for layer i S : the rate of linear increase in BW

Dropping a Layer: - once a backoff occurs, if the total amount of buffering at the receiver is less than the estimated required buffering for recovery (i.e., the area of triangle cde in fig.5), the correct action is to immediately drop the highest layer - if the buffering is still insufficient, the server should interactively drop the highest layer until the amount of buffering is sufficient

while ( n a C > R + 2S i=0 to na-1 buf i )   do n a = n a - 1

Optimal Interlayer Buffer Allocation: - due to the decoding constraint in hierarchical encoding, each additional layer depends on all the lower layers - a buffer allocation mechanism should provide higher protection for lower layers by allocating a higher share of total buffering to them

- the minimum number of buffering layers that are needed for successful recovery from short-term reduction in available BW can be determined as: n b = n a – [R/(2*C)], n a C > R/2 n b = 0, n a C <= R/2 wheren b : # of buffering layers n a : # of active layers

- the consumption rate of a layer must be supplied either from the network or from the buffer or a combination of the two -If it is supplied entirely from the buffer, that layer’s buffer is draining at consumption rate C.

The optimal amount of buffering for layer i is: if i < n b – 1, Buf i,opt = C [C (2n a – 2i - 1) - R] 2S if i = n b – 1 (the top layer), Buf i,opt = C [n a C – (R/2) – (i*C)] 2 2S

Fine-Grain Bandwidth Allocation: - the server can control the filling and draining pattern of receiver’s buffers by proper fine-grain bandwidth allocation among active layers - fine-grain bandwidth allocation is performed by assigning the next packet to a particular layer

- the main challenge is that the optimal interlayer buffer allocation depends on the transmission rate at the time of a priori backoff (R), which is not known a priori because a backoff may occur at any random time - to tackle this problem, during the filling phase, the server utilizes extra BW to progressively fill receiver’s buffers step-wise up to an optimal state in a step-wisefashion

- the server maintains an image of the receiver’s buffer state, which is continuously updated based on the playout information included in ACK pkts

- During the filling phase, the extra BW is allocated among buffering layers on a per-packet basis through the following steps assuming a backoff will occur immediately: 1. if we keep only one layer (L 0 ), is there sufficient buffering with optimal distribution to recover ? * if there is no sufficient buffering, the next packet is assigned to L 0 until this condition is met and then the second step is started

2. if we keep only two layers (L 0, L 1 ), is there sufficient buffering with optimal distribution to recover ? * if there is no sufficient buffering, the next packet is assigned to L0 until it reaches its optimal level. Then, the server starts sending packets for L1 until both layers have the optimal level of buffering to survive - we then start a new step and increase the number of expected surviving layers, calculate a new optimal buffer distribution and sequentially their buffers up to the new optimal level - this process is repeated until all layers can survive a single backoff

-During the draining phase, BW share plus draining rate for each layer is equal to its consumption rate. - Thus, maximally efficient buffering results in the upper layers being supplied from the network during the draining phase, while the lower layers are supplied from their buffers

If we would like to survive more than one backoff (K max > 1): - During the filling phase:

- During the draining phase: * due to one or more backoffs * reverse the filling phase * identify between which two steps we are currently located. This determines how many layers should be dropped due to lack of sufficient buffering * then, we traverse through the steps in the reverse order to determine which buffering layers must be drained and by how much * the amount and pattern of draining is then controlled by fine-grain interlayer BW allocation by the server